An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

An Anonymous System Based on Random Virtual Proxy Mutation

Anonymous systems are usually used to protect users\u27 privacy in network communication. However, even in the low-latency Tor system, it is accompanied by network communication performance degradation, which makes users have to give up using the anonymity system in many applications. Therefore, we propose a novel anonymity system with rotated multi-path accompanying virtual proxy mutation for data transmission. Unlike onion routing, in our system the randomly generated virtual proxies take over the address isolation executing directly on the network layer and expand the anonymity space to all terminals in the network. With the optimal algorithm of selecting the path, the network communication performance improved significantly also. The verification experiments show that the anonymity system terminal sends and receives data at 500 kbps, and only a slight delay jitter occurs at the receiving end, and the other network performance is not significantly reduced

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity

LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding

The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a lack of effective solutions for training TTFS-based spiking neural network (SNN). In this paper, we put forward a simple yet effective network conversion algorithm, which is referred to as LC-TTFS, by addressing two main problems that hinder an effective conversion from a high-performance artificial neural network (ANN) to a TTFS-based SNN. We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks, including image classification, image reconstruction, and speech enhancement. With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs. The study, therefore, paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms

Long Short-term Memory with Two-Compartment Spiking Neuron

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates an extended memory capacity. To address this challenge, we propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our model incorporates carefully designed somatic and dendritic compartments that are tailored to retain short- and long-term memories. The theoretical analysis further confirms its effectiveness in addressing the notorious vanishing gradient problem. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines

The treatment of tuberculosis in the upper thoracic spine using the small incision technique through the third rib

BackgroundThe complex anatomical structure of the upper thoracic spine makes it challenging to achieve surgical exposure, resulting in significant surgical risks and difficulties. Posterior surgery alone fails to adequately address and reconstruct upper thoracic lesions due to limited exposure. While the anterior approach offers advantages in fully exposing the anterior thoracic lesions, the surgical procedure itself is highly intricate. Although there exist various anterior approaches for the upper thoracic spine, the incidence of upper thoracic spine lesions is relatively low. Consequently, there are limited reports on the treatment and reconstruction of upper thoracic spine lesions using the third rib small incision approach in the context of upper thoracic tuberculosis.MethodsWe collected data from four patients with upper thoracic tuberculosis who were admitted to our department between July 2017 and November 2022. The treatment for upper thoracic tuberculosis involved utilizing the third rib small incision approach, which included two cases of thoracic 3–4 vertebral tuberculosis, one case of thoracic 4 vertebral tuberculosis, and one case of thoracic 5 vertebral tuberculosis. Among the patients, three were positioned in the left lateral position, while one was positioned in the right lateral position. Prior to admission, all four patients received a two-week course of oral medication, consisting of isoniazid, rifampicin, pyrazinamide, and ethambutol. After the surgical procedure, they continued receiving anti-tuberculosis treatment for a duration of 12 months.ResultsThe average duration of the surgical procedure was 150 min, with an average blood loss of 500 ml. One patient exhibited symptoms of brachial plexus injury, which gradually improved after careful observation. All patients experienced primary wound healing, and no complications such as pulmonary infection, respiratory failure, or other adverse events were observed. Additionally, one patient showed elevated transaminase levels, leading to a modification in the anti-tuberculosis drug regimen from quadruple therapy to triple therapy.ConclusionThe treatment of upper thoracic tuberculosis through the third rib small incision technique is a very good surgical approach, which has the advantages of safety and effectiveness

A Genetic Variant in the Promoter Region of miR-106b-25 Cluster and Risk of HBV Infection and Hepatocellular Carcinoma

BACKGROUND: MiR-106b-25 cluster, hosted in intron 13 of MCM7, may play integral roles in diverse processes including immune response and tumorigenesis. A single nucleotide polymorphism (SNP), rs999885, is located in the promoter region of MCM7. METHODS: We performed a case-control study including 1300 HBV-positive hepatocellular carcinoma (HCC) cases, 1344 HBV persistent carriers and 1344 subjects with HBV natural clearance to test the association between rs999885 and the risk of HBV persistent infection and HCC. We also investigated the genotype-expression correlation between rs999885 and miR-106b-25 cluster in 25 pairs of HCC and adjacent non-tumor liver tissues. RESULTS: Compared with the HBV natural clearance subjects carrying rs999885 AA genotype, those with AG/GG genotypes had a decreased risk of chronic HBV infection with an adjusted odds ratio (OR) of 0.79 [95% confidence intervals (CIs) = 0.67-0.93]. However, the AG/GG genotypes were significantly associated with an increased HCC risk in HBV persistent carriers (adjusted OR = 1.25, 95% CIs = 1.06-1.47). Expression analysis revealed that the expression level of miR-106b-25 cluster was significantly higher in AG/GG carriers than those in AA carriers in non-tumor liver tissues. CONCLUSIONS: These findings indicate that the A to G base change of rs999885 may provide a protective effect against chronic HBV infection but an increased risk for HCC in HBV persistent carriers by altering the expression of the miR-106b-25 cluster

Three-Dimensional Simulation of the Shrinkage Behavior of Injection-Molded Poly Lactic Acid (PLA): Effects of Temperature, Shear Rate and Part Thickness

The effects of injection temperature, shear and part thickness on the linear shrinkage of injection-molded poly (lactic acid) (PLA) were intensively analyzed using the Autodesk Moldflow software. The obtained results showed that both melt temperature and shear rate had obvious effects on the linear shrinkage of PLA, i.e., the linear shrinkage of PLA increases significantly with the increase of melt temperature and shear rate. In addition, the shrinkage of high-crystallinity PLA was remarkably larger than that of low-crystallinity PLA, and thin-walled parts was larger than thick-walled ones in shrinkage