Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons

As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term potentiation (ALTP) phenomenon suggests that in addition to learning mechanisms between neurons, there are associative effects within neurons. However, most existing methods only focus on the former and lack exploration of the internal association effects. In this paper, we propose a novel Adaptive Internal Association~(AIA) neuron model to establish previously ignored influences within neurons. Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time. In addition, we employ weighted weights to measure internal associations and introduce intermediate caches to reduce the volatility of associations. Extensive experiments on prevailing neuromorphic datasets show that the proposed method can potentiate or depress the firing of spikes more specifically, resulting in better performance with fewer spikes. It is worth noting that without adding any parameters at inference, the AIA model achieves state-of-the-art performance on DVS-CIFAR10~(83.9\%) and N-CARS~(95.64\%) datasets.Comment: Accepted by ICASSP 202

Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments

Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to the spatiotemporal characteristics, novel data augmentations are required to process the unconventional visual signals of these cameras. In this paper, we propose a novel Event SpatioTemporal Fragments (ESTF) augmentation method. It preserves the continuity of neuromorphic data by drifting or inverting fragments of the spatiotemporal event stream to simulate the disturbance of brightness variations, leading to more robust spiking neural networks. Extensive experiments are performed on prevailing neuromorphic datasets. It turns out that ESTF provides substantial improvements over pure geometric transformations and outperforms other event data augmentation methods. It is worth noting that the SNNs with ESTF achieve the state-of-the-art accuracy of 83.9\% on the CIFAR10-DVS dataset.Comment: Accepted by ICASSP 202

Construction and Evaluation of Mandarin Multimodal Emotional Speech Database

A multi-modal emotional speech Mandarin database including articulatory kinematics, acoustics, glottal and facial micro-expressions is designed and established, which is described in detail from the aspects of corpus design, subject selection, recording details and data processing. Where signals are labeled with discrete emotion labels (neutral, happy, pleasant, indifferent, angry, sad, grief) and dimensional emotion labels (pleasure, arousal, dominance). In this paper, the validity of dimension annotation is verified by statistical analysis of dimension annotation data. The SCL-90 scale data of annotators are verified and combined with PAD annotation data for analysis, so as to explore the internal relationship between the outlier phenomenon in annotation and the psychological state of annotators. In order to verify the speech quality and emotion discrimination of the database, this paper uses 3 basic models of SVM, CNN and DNN to calculate the recognition rate of these seven emotions. The results show that the average recognition rate of seven emotions is about 82% when using acoustic data alone. When using glottal data alone, the average recognition rate is about 72%. Using kinematics data alone, the average recognition rate also reaches 55.7%. Therefore, the database is of high quality and can be used as an important source for speech analysis research, especially for the task of multimodal emotional speech analysis

A Combined Experimental and Computational Study of the Cu/C (sp2) Interface

Interface optimization is the most important and eternal research issue in preparation of the metal matrix composites (MMCs). For nano sp2-carbon material (NSCM)/metal composites, interfacial precipitates are usually formed intentionally or unintentionally, however, the effect of the interface structure and precipitates on the electron transport properties is still unclear, which is especially important for Cu-based material due to the electronic and electrical applications. In this paper, a series of interface models were constructed based on the transmission electron microscopy (TEM) observation of NSCM/Cu composite and calculated through density functional theory (DFT). The geometric structure, interfacial charge transfer, work function, Bader charges, electron differential density distribution and electronic density of states of Cu/graphene (GR), Cu2O/GR, Cu/Cu2O and Cu/Cu2O/GR interfaces were discussed in detail, we conclude that the Cu2O precipitates at the Cu/GR interface can reduce the average distance and increase the binding energy between Cu and GR. Besides, the formation of Cu2O can improve the electronic transport between Cu2O and copper instead of the weak binding of the Cu and graphene, but Schottky barrier at the interface remains an obstacle need to be overcome. The results can provide reference for the interface design of MMCs and the improvement of the composite properties

Identification of resection plane for anatomical liver resection using ultrasonography-guided needle insertion

PurposesTo set up an easy-handled and precise delineation of resection plane for hepatic anatomical resection (AR).MethodsCases of AR using ultrasonography-guided needle insertion to trace the target hepatic vein for delineation of resection planes [new technique (NT) group, n = 22] were retrospectively compared with those without implementation of this surgical technique [traditional technique (TT) group, n = 29] in terms of perioperative courses and surgical outcomes.ResultsThe target hepatic vein was successfully exposed in all patients of the NT group, compared with a success rate of 79.3% in the TT group (P < 0.05). The average operation time and intraoperative blood loss were 280 ± 32 min and 550 ± 65 ml, respectively, in the NT group. No blood transfusion was required in either group. The postoperative morbidities (bile leakage and peritoneal effusion) were similar between groups. No mortality within 90 days was observed.ConclusionsUltrasonography-guided needle insertion is a convenient, safe and efficient surgical approach to define a resection plane for conducting AR

Kinetic Theory-Based Methods in Fluid Dynamics

Kinetic theory stems from the statistical mechanics established at the mesoscopic scale [...

Kinetic Theory-Based Methods in Fluid Dynamics

Kinetic theory stems from the statistical mechanics established at the mesoscopic scale [...

Consistent boundary conditions of the multiple-relaxation-time lattice Boltzmann method for convection–diffusion equations

In this work, the Dirichlet, Neumann and linear Robin conditions for the convection–diffusion equation (CDE) lattice Boltzmann (LB) method is investigated and a second-order boundary scheme is proposed for the D2Q9 multiple-relaxation-time (MRT) LB model. With the proposed scheme, consistent implementations are developed for the three kinds of macroscopic boundary constraints considered at both straight and curved boundaries. The second-order accuracy of the present boundary scheme is firstly demonstrated by the theoretical derivations and then confirmed by the numerical validations. Notably, the advantages of the present boundary scheme lie in its locality and consistency, i.e., no information from the neighboring fluid nodes is required in the practical treatments, and all three kinds of boundary conditions are directly implemented without degrading the Robin condition to the Dirichlet or Neumann condition

Numerical study on the axial segregation dynamics of a binary-size granular mixture in a three-dimensional rotating drum

Granular materials are ubiquitous in our daily life and inherent in multitudinous industrial processes. Differences in the granular properties such as size and density inevitably induce segregation. By means of the discrete element method, a binary-size mixture in a three-dimensional rotating drum is numerically simulated to explore the segregation dynamics of the granular material along the axial direction. Snapshots of the distribution of the two particle types in the rotating drum are presented with respect to time to illustrate the spatial evolution of the size-induced segregation structure. The space-time plots of various axial characteristics indicate that (i) radial segregation does not affect the axial distribution of total mass and mass fraction, but axial segregation leads to the formation of axial bands; (ii) greater non-dimensionalized collision forces for both the large and small particles develop where the large particles dominate; and (iii) axial segregation gives rise to the variation of the gyration radii of both particle types along the drum length. In addition, axial flow of both particle types in both directions indicates the dynamic axial exchanges, and the effect of the end walls on the axial flow direction is limited to less than 25% of the drum length from the end walls.NRF (Natl Research Foundation, S’pore)Published versio