In recent years, it has been observed that there is an increasing rate of road accidents due to the low vigilance of drivers. Thus, the estimation of drivers' vigilance state plays a significant role ...in public transportation safety. We have adopted a feature fusion strategy that combines the electroencephalogram (EEG) signals collected from various sites of the human brain, including forehead, temporal, and posterior and forehead electrooculogram (forehead-EOG) signals, to address this factor. The level of vigilance is predicted through a new learning model known as double-layered neural network with subnetwork nodes (DNNSNs), which comprises several subnetwork nodes, and each node in turn is composed of many hidden nodes that have various capabilities of feature selection (dimension reduced), feature learning, etc. The proposed single modality that uses only forehead-EOG signal exhibits a mean root-mean-square error (RMSE) of 0.12 and a mean Pearson product-moment correlation coefficient (COR) of 0.78. On one hand, an EEG signal achieved a mean RMSE of 0.13 and a mean COR of 0.72. Whereas, on the other, the proposed multimodality achieved values of 0.09 and 0.85 for the mean RMSE and the mean COR, respectively. Experimental results show that the proposed DNNSN with multimodality fusion outperforms the model with single modality for vigilance estimation due to the complementary information between forehead-EOG and EEG. After a favorable learning rate was applied to the input layer, the mean RMSE/COR improved to 0.11/0.79, 0.12/0.74, and 0.08/0.86, respectively. Hence, this quantitative analysis proves that the proposed method provides better feasibility and efficiency learning capability and surmounts other state-of-the-art techniques.
Accurate intracellular cholesterol traffic plays crucial roles. Niemann Pick type C (NPC) proteins NPC1 and NPC2, are two lysosomal cholesterol transporters that mediate the cholesterol exit from ...lysosomes. However, other proteins involved in this process remain poorly defined. Here we find that the previously unannotated protein TMEM241 is required for cholesterol egressing from lysosomes through amphotericin B-based genome-wide CRISPR-Cas9 knockout screening. Ablation of TMEM241 caused impaired sorting of NPC2, a protein utilizes the mannose-6-phosphate (M6P) modification for lysosomal targeting, resulting in cholesterol accumulation in the lysosomes. TMEM241 is a member of solute transporters 35 (SLC35) nucleotide sugar transporters family and localizes on the cis-Golgi network. Our data indicate that TMEM241 transports UDP-N-acetylglucosamine (UDP-GlcNAc) into Golgi lumen and UDP-GlcNAc is used for the M6P modification of proteins including NPC2. Furthermore, Tmem241-deficient mice display cholesterol accumulation in pulmonary cells and behave pulmonary injury and hypokinesia. Taken together, we demonstrate that TMEM241 is a Golgi-localized UDP-GlcNAc transporter and loss of TMEM241 causes cholesterol accumulation in lysosomes because of the impaired M6P-dependent lysosomal targeting of NPC2.
Simplified Molecular Input Line Entry System (SMILES) provides a text-based encoding method to describe the structure of chemical species and formulize general chemical reactions. Considering that ...chemical reactions have been represented in a language form, we present a symbol only model to generally predict the yield of organic synthesis reaction without considering complex quantum physical modeling or chemistry knowledge. Our model is the first deep neural network application that treats chemical reaction text segments as embedding representation to the most recent deep natural language processing. Experimental results show our model can effectively predict chemical reactions, which achieves a high accuracy of 99.76% on practicality judgment and the Root Mean Square Error (RMSE) is around 0.2 for yield prediction. Our work shows the great potential for automatic yield prediction for organic reactions under general conditions and further applications in synthesis path prediction with the least modeling cost.
Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection ...algorithm (CSA) has been widely used for many optimization problems. CSA mainly generates new schemes by hyper-mutation operators which simulate the immune response process. However, these hyper-mutation operators, which usually perturb the antibodies in population, are semi-blind and not effective enough for complex optimization problems. In this paper, we propose a hybrid learning clonal selection algorithm (HLCSA) by incorporating two learning mechanisms, Baldwinian learning and orthogonal learning, into CSA to guide the immune response process. Specifically, (1) Baldwinian learning is used to direct the genotypic changes based on the Baldwin effect, and this operator can enhance the antibody information by employing other antibodies’ information to alter the search space; (2) Orthogonal learning operator is used to search the space defined by one antibody and its best Baldwinian learning vector. In HLCSA, the Baldwinian learning works for exploration (global search) while the orthogonal learning for exploitation (local refinement). Therefore, orthogonal learning can be viewed as the compensation for the search ability of Baldwinian learning. In order to validate the effectiveness of the proposed algorithm, a suite of sixteen benchmark test problems are fed into HLCSA. Experimental results show that HLCSA performs very well in solving most of the optimization problems. Therefore, HLCSA is an effective and robust algorithm for optimization.
Larger n-gram language models (LMs) perform better in statistical machine translation (SMT). However, the existing approaches have two main drawbacks for constructing larger LMs: 1) it is not ...convenient to obtain larger corpora in the same domain as the bilingual parallel corpora in SMT; 2) most of the previous studies focus on monolingual information from the target corpora only, and redundant n-grams have not been fully utilized in SMT. Nowadays, continuous-space language model (CSLM), especially neural network language model (NNLM), has been shown great improvement in the estimation accuracies of the probabilities for predicting the target words. However, most of these CSLM and NNLM approaches still consider monolingual information only or require additional corpus. In this paper, we propose a novel neural network based bilingual LM growing method. Compared to the existing approaches, the proposed method enables us to use bilingual parallel corpus for LM growing in SMT. The results show that our new method outperforms the existing approaches on both SMT performance and computational efficiency significantly.
Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are ...mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective.
Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition.
From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that the
and
bands exhibit decreasing activities for Chinese, while for German and French,
and
bands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures.
Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.
The absorption characteristics of one‐dimensional photonic crystals (1DPC) consisting of an ordinary dielectric and graphene–dielectric hyperbolic metamaterials in terahertz (THz) are theoretically ...analyzed and numerically simulated using the transfer matrix method. The perfect absorption peaks can be achieved in the structure designed in our research. In the 1DPC, the dielectric layers are embedded into graphene layers and the optical axis of the graphene–dielectric hyperbolic metamaterials is normal to the graphene layers. The graphene–dielectric layers are seen as anisotropic effective medium and possess the properties of hyperbolic dispersion in THz frequency band. The locations of perfect absorptance peaks are dependent on chemical potential, the thicknesses of dielectric layers and the inclined angle. The positions of absorption peaks are blue‐shifted with increasing the chemical potential and red‐shifted by increasing the thicknesses of the dielectric layers. We testified that the orientation of the optical axis of the graphene–dielectric layers has prominent effect on the locations of the perfect absorption peaks. Moreover, multiband absorption peaks can be obtained by introducing more structure as given in our research with different parameters. The controllable perfect absorption achieved at the far infrared ray (FIR) frequencies could be applied in the design of multichannel perfect absorption filters.
The absorption characteristics of one‐dimensional photonic crystals consisting of an ordinary dielectric and graphene‐dielectric hyperbolic metamaterials in the terahertz band are theoretically analyzed. Perfect absorption peaks are achieved in the structure. The positions of absorption peaks are adjusted by chemical potential, the thicknesses of dielectric layers and the orientation of the optical axis. Moreover, multiband absorption peaks are obtained in the structure.
Cholesterol biosynthesis is tightly regulated in the cell. For example, high sterol concentrations can stimulate degradation of the rate-limiting cholesterol biosynthetic enzyme ...3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoA reductase, HMGCR). HMGCR is broken down by the endoplasmic reticulum membrane–associated protein complexes consisting of insulin-induced genes (Insigs) and the E3 ubiquitin ligase gp78. Here we found that HMGCR degradation is partially blunted in Chinese hamster ovary (CHO) cells lacking gp78 (gp78-KO). To identify other ubiquitin ligase(s) that may function together with gp78 in triggering HMGCR degradation, we performed a small-scale short hairpin RNA–based screening targeting endoplasmic reticulum–localized E3s. We found that knockdown of both ring finger protein 145 (Rnf145) and gp78 genes abrogates sterol-induced degradation of HMGCR in CHO cells. We also observed that RNF145 interacts with Insig-1 and -2 proteins and ubiquitinates HMGCR. Moreover, the tetrapeptide sequence YLYF in the sterol-sensing domain and the Cys-537 residue in the RING finger domain were essential for RNF145 binding to Insigs and RNF145 E3 activity, respectively. Of note, amino acid substitutions in the YLYF or of Cys-537 completely abolished RNF145-mediated HMGCR degradation. In summary, our study reveals that RNF145, along with gp78, promotes HMGCR degradation in response to elevated sterol levels and identifies residues essential for RNF145 function.
Two-Dimensional Embedded Fuzzy Data Clustering Peng, Yong; Chen, Keding; Nie, Feiping ...
IEEE transactions on emerging topics in computational intelligence,
08/2023, Letnik:
7, Številka:
4
Journal Article
Recenzirano
Fuzzy <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-means (FKM) is a popular clustering method by assigning data points into respective clusters with uncertainty measured ...by the membership degree. Usually, FKM performs clustering according to the distance between data points in the original space, which might contain undesirable noises and redundant features; therefore, the underlying data semantic connections cannot be accurately captured. Moreover, the vectorized representation of the two-dimensional data such as image inevitably leads to the loss of structural information. In this paper, we propose a novel FKM method termed two-dimensional embedded fuzzy data clustering (2DEFC) which has two merits. First, 2DEFC directly takes 2D data as input without vectorizing them in order to retain more structural information of data. Second, the two subspace projection matrices are jointly optimized with the data membership degree for better collaborating with each other, which effectively avoids the sub-optimality limitation caused by the conventional mode of sequentially performing dimensionality reduction and clustering. An efficient algorithm is proposed to optimize the 2DEFC objective function. Besides, we provide comprehensive analysis on 2DEFC including its convergence behavior, computational complexity, and the fuzzy weighting exponent. Extensive comparative studies on benchmark 2D data sets demonstrate that the competitive performance of 2DEFC in 2D data clustering.
Most of the existing graph-based clustering models performed clustering by adopting a two-stage strategy which first completes the spectral embedding from a given fixed graph and then resorts to ...other clustering methods such as <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>means to achieve discrete cluster results. On one hand, such a discretization operation easily causes that the obtained results deviate far from the true solution. On the other hand, clustering performance heavily relies on the quality of graph; therefore, the fixed graph is usually not optimal enough. In addition, clustering by separated steps inevitably breaks the underlying connections among the graph construction, spectral embedding and discretization. To address these drawbacks, in this paper, we propose a new spectral clustering model termed JGSED which integrates the graph construction, spectral embedding and spectral rotation together into a unified objective. JGSED is an end-to-end framework to directly take data as input and output the final binary cluster indicator matrix. An efficient algorithm is proposed to optimize the model variables in JGSED, which can be co-evolved towards the optimum. Extensive experiments are conducted on both synthetic and real data sets and the results demonstrate that JGSED outperforms the other state-of-the-art spectral clustering models, indicating the effectiveness of joint optimization.
PDF
