A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that simultaneously trains synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher accuracies on various static and neuromorphic datasets than SNNs trained with two single-learning models of the synaptic learning (SL) and the threshold learning (TL). During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework (JDF). Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic selection process of salient regions in biological vision systems. Although mechanisms of visual attention have achieved great success in computer vision, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we here propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing historically accumulated spatial-channel information. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and other two SNN models with degenerated attention modules, but also achieves competitive accuracy with existing state-of-the-art methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability to incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that appropriately incorporating cognitive mechanisms of the brain may provide a promising approach to elevate the capability of SNNs.Comment: 12 pages, 8 figures, 5 tabes; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks

BackgroundBladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking.MethodsWe downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking.ResultsPMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins.ConclusionsOur PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa

Emotional warmth and cyberbullying perpetration attitudes in college students: Mediation of trait gratitude and empathy.

Based on Social Learning Theory and the General Aggression Model, this study aims to explore the relationship between parental emotional warmth and the cyberbullying perpetration attitudes of college students and the mediating roles of trait gratitude and empathy. Using the stratified cluster random sampling method, 1198 college students (716 boys and 482 girls with an average age of 20.44 years) were tested using the subscale of the Parenting Styles Instrument, the Basic Empathy Scale, the Gratitude Questionnaire-6, and the Cyberbullying Attitude Questionnaire. Results: Emotional warmth, trait gratitude, cognitive empathy, and affective empathy all demonstrated significantly positive relationships with each other (rs from .175 to .403, ps < 0.01) and negative correlations with cyberbullying perpetration attitudes (rs from -.137 to -.306, ps < 0.01). Emotional warmth can exert an impact on cyberbullying perpetration attitudes through three fully mediating paths: the mediating roles of trait gratitude (41.91% of the total effect), cognitive empathy (14.5% of the total effect), and the chain mediating roles of trait gratitude-cognitive empathy (19.5% of the total effect). The results may have important implications for future studies to develop effective interventions for cyberbullying

Impacts of Air-Sea Energy Transfer on Typhoon Modelling

The Coupled Ocean-Atmosphere-Wave-Sediment Transport model has been used to simulate Super Typhoon Yutu (2018). The impacts of four momentum transfer parameterization schemes (COARE, TY, OT, and DN) and three heat transfer parameterization schemes (COARE, GR, and ZK) on typhoon modelling have been studied by means of the track, intensity, and radial structure of typhoon. The results show that the track of Yutu is not sensitive to the choice of parameterization scheme, while the combinations of different parameterization schemes affect the intensity of Yutu. Among the four momentum flux parameterization schemes, three wave-state-based schemes (TY, OT, and DN) provide better intensity results than the wind-speed-based COARE scheme, but the differences between the three wave-state-based schemes are not obvious. Among the three heat flux parameterization schemes, the results of the GR scheme are slightly better than those of the COARE scheme, and both the GR and COARE schemes are significantly better than the ZK scheme, from which the intensity of Yutu is underpredicted obviously. The influence of the combination of different parameterization schemes on the intensity of the typhoon is also reflected in the radial structure of the typhoon, and the radial structure of the typhoon simulated by experiments with stronger typhoon intensity also develops faster. Differences of intensity between experiments are due mainly to the differences in sea surface heat flux, the enthalpy transferred from sea surface to the atmosphere has a significant impact on the bottom atmosphere wind field, and there is a strong correspondence between the distribution of enthalpy flux and the bottom wind field

Chiral Dual-Core Photonic Crystal Fiber for an Efficient Circular Polarization Beam Splitter

As a function of a circular polarization beam splitter (CPBS), combining a linear polarization beam splitter with a quarter-wave plate results in a polarization error in a circular polarization fiber-optic circuit. To relieve the error, chiral dual-core photonic crystal fiber (DC-PCF) is investigated as a kind of an efficient circular polarization beam splitter by using the chiral plane-wave expansion (PWE) method. On the basis of the competitive effect in polarization and coupling length between the circular asymmetry of the structure and the chirality of the medium, the effects of the structure and the chirality are analyzed. The numerical results demonstrate that a CPBS needs the weak circular asymmetry in its structure and a relatively stronger chirality of the medium. Then, a kind of CPBS based on chiral DC-PCF is designed with weaker chirality, with a central wavelength of 1.55 μm. The simulation shows the superior performance of having a shorter coupling length and a higher extinction ratio. Furthermore, the dual-wavelength of 1.55 μm and 1.30 μm with left-circular polarization can further be separated by the corresponding chiral DC-PCF. The results show promising applications for the circular polarized multiplexer/demultiplexer in fiber laser communication systems

Optimization of (α + β) microstructure and trade-off between strength and toughness: Based on Mo[eq] and d electron theory in β-Ti alloy

To optimize the (α + β) microstructure and find a trade-off between strength and toughness, Ti-xMo-4Al-4Zr-3Nb-2Cr-1Fe alloys were prepared according to Mo[eq] and d electron theory. Microstructure of α phase and dislocation was observed, and the related mechanisms were determined. Results show that the relative content of β phase increases by adjusting Mo content. Length-width ratios of αp and αs phases decrease from 8.8 to 6 and 10.8 to 9.1 as Mo increases from 5 to 6 wt%. When the Mo content increases further, length–width ratio increases. The dislocation density reaches its maximum at 6Mo. The low diffusion rate of Mo and refined β grains causes the refinement of the α phase. The increase of grain boundary and the appearance of lattice distortion increase the dislocation density, but the formation of twins consumes partial dislocation. The tensile strength first increases and then decreases, reaching a maximum of 1326 MPa at 6Mo. The toughness of 6Mo alloy is 85 MPa·m1/2. The strength increases by 12% while the toughness only decreases by 4%. The precipitation strengthening caused by the optimization of the (α + β) microstructure and the dislocation strengthening caused by the increased dislocations are the determining mechanism of the trade-off between strength and toughness

Additively manufactured high-energy-absorption metamaterials with artificially engineered distribution of bio-inspired hierarchical microstructures

There is an increasing demand of protective lightweight components in aerospace industries, and the high flexibility of additive manufacturing (AM) enables the design of complex structures to achieve such goal. In this study, a novel high-energy-absorption spherical hollow structure (SHS) was first engineered with a layer-wise failure mode and crystal-inspired grain boundaries through the variation of its hierarchical microstructures. To engineer the strength distribution of SHS, the mechanical properties of its spherical unit cells with bending-dominated and stretch-dominated honeycomb microstructures was experimentally studied with respect to different microstructural densities. Simulations were also performed to further reveal their failure mechanisms. Based on the relationship between the microstructural densities and the mechanical responses of these unit cells, a failure mode engineering method was proposed to artificially control the failure sequence of the lattice structure through a microstructural-controlled strength distribution. Here, we demonstrated a laminated failure mode composite hierarchical SHS lattice with crystal-inspired bending and stretch-dominated grains was developed using AM. Compared to different energy-absorption material designs with similar density, the quasi-static compressive results indicated that a hierarchical SHS lattice possesses a 72% improvement in the specific energy absorption, a 50% higher density-normalized plateau stress owing to the constraining effect of its mesoscale grain boundaries, and an increased number of intensively engineered laminated failure levels. This manuscript proposes a new design paradigm of AM high energy-absorption lattice structure for different protective applications

Refinement of αs phase and formation of nano-twins of Ti–7Mo–4Al–3Nb–2Cr alloyed by Zr element

To refine the αs phase and form the nano twins, different contents of Zr were added to as-cast Ti–7Mo–4Al–3Nb–2Cr-xZr alloys. Phase and microstructure evolution, mechanical properties and related mechanisms were systematically studied. Results show that as the Zr increases from 0 to 8 wt%, only α and β phases exist and the peak of β (110) shifts from 39.5 to 39.1° by XRD. The volume fraction of the αs phase changes from 75.4 to 73.9%, and its length decreases from 0.62 to 0.22 μm. More dislocations appear in the α/β interface and the growth twin of {10 1¯ 1}α  type with a width of ∼20 nm forms in α phase as observed by TEM. The refinement of the αs phase is due to the addition of Zr to promote nucleation and inhibit the growth rate. With increasing Zr content from 0 to 8 wt%, the strength increases from 961 to 1303 MPa, while the toughness decreases from 77 to 62 MPa m1/2. The fracture toughness decreases by 19.5%, while the strength increases by 35.6%. The refinement of αs phase, the solubilization of the Zr in the matrix, the increase of dislocation in the α/β interface, and the formation of nano twins are the main reasons for improving the strength and toughness of the studied alloys

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification