Learning Generative ConvNets via Multi-grid Modeling and Sampling

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from the model. Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image. The synthesized image at each subsequent grid is obtained by a finite-step MCMC initialized from the synthesized image generated at the previous coarser grid. After obtaining the synthesized examples, the parameters of the models at multiple grids are updated separately and simultaneously based on the differences between synthesized and observed examples. We show that this multi-grid method can learn realistic energy-based generative ConvNet models, and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201

Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data

Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.Comment: 6 pages, 3 figures, HCI International 2023 Poste

IMPROVED DESIGN OF DTW AND GMM CASCADED ARABIC SPEAKER

In this paper, we discuss about the design, implementation and assessment of a two-stage Arabic speaker recognition system, which aims to recognize a target Arabic speaker among several people. The first stage uses improved DTW (Dynamic Time Warping) algorithm and the second stage uses SA-KM-based GMM (Gaussian Mixture Model). MFCC (Mel Frequency Cepstral Coefficients) and its differences form, as acoustic feature, are extracted from the sample speeches. DTW provides three most possible speakers and then the recognition results are conveyed to GMM training processes. A specified similarity assessment algorithm, KL distance, is applied to find the best match with the target speaker. Experimental results show that text-independent recognition rate of the cascaded system reaches 90 percent

OCT4B1 Regulates the Cellular Stress Response of Human Dental Pulp Cells with Inflammation

Introduction. Infection and apoptosis are combined triggers for inflammation in dental tissues. Octamer-binding transcription factor 4-B1 (OCT4B1), a novel spliced variant of OCT4 family, could respond to the cellular stress and possess antiapoptotic property. However, its specific role in dental pulpitis remains unknown. Methods. To investigate the effect of OCT4B1 on inflammation of dental pulp cells (DPCs), its expression in inflamed dental pulp tissues and DPCs was examined by in situ hybridization, real-time PCR, and FISH assay. OCT4B1 overexpressed DPCs model was established, confirmed by western blot and immunofluorescence staining, and then stimulated with Lipopolysaccharide (LPS). Apoptotic rate was determined by Hoechst/PI staining and FACS. Cell survival rate was calculated by CCK8 assay. Results. In situ hybridization, real-time PCR, and FISH assay revealed that OCT4B1 was extensively expressed in inflamed dental pulp tissues and DPCs with LPS stimulation. Western blot and immunofluorescence staining showed the expression of OCT4B1 and OCT4B increased after OCT4B1 transfection. Hoechst/PI staining and FACS demonstrated that less red/blue fluorescence was detected and apoptotic percentage decreased (3.45%) after transfection. CCK8 demonstrated that the survival rate of pCDH-OCT4B1-flag cells increased. Conclusions. OCT4B1 plays an essential role in inflammation and apoptosis of DPCs. OCT4B might operate synergistically with OCT4B1 to reduce apoptosis