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A general population model with Allee effect driven by correlated additive and multiplicative white noises is considered. This paper aims to investigate noise-induced phenomenological bifurcation (P-bifurcation) and the influence of noises on the population model. With the help of Fokker-Planck equation, we obtain the stationary probability distribution (SPD) of the model, and find that the shape of SPD experiences a transition from one structure to another when the noise intensity passes through a critical value, i.e., the P-bifurcation occurs. Moreover, detailed analysis and simulations for stochastic logistic-like models with weak and strong Allee effects show that the correlated noises have complex effects on the eventual distribution of population size. This paper aims to investigate noise-induced phenomenological bifurcation (P-bifurcation) for a general population model with Allee effect driven by correlated additive and multiplicative white noises. We find that the shape of SPD experiences a transition from one structure to another when the noise intensity passes through a critical value, i.e., the P-bifurcation occurs.

With the rapid growth of the networked data, the study of community detection is drawing increasing attention of researchers. A number of algorithms have been proposed and some of them have been well applied in many research fields, such as recommendation systems, information retrieval, etc. Traditionally, the community detection methods mainly use the knowledge of the topological structure which contains the most important clue for finding potential groups or communities. However, as we know, a wealth of content information exists on the nodes in real-world networks, and may help for community detection. Considering the above problem, we introduce a novel community detection method under the framework of nonnegative matrix factorization (NMF), and adopt the idea that two nodes with similar content will be most likely to belong to the same community to achieve the incorporation of links and node contents, i.e., we employ a graph regularization to penalize the dissimilarity of nodes denoted by community memberships. Besides, we introduce an intuitive manifold learning strategy to recover the intrinsic geometrical structure of the content information, i.e., K-near neighbor consistency. In addition, we found that, there are still drawbacks in this framework due to it does not consider the heterogeneous distribution of node degrees. This heterogeneous distribution can affect the function of graph regularization and isolates the original community memberships. We first proposed the node popularities satisfying the above interpretation and develop a new NMF-based model, named as Combination of Links and Node Contents for Community Discovery (CLNCCD). The experiments on both artificial and real-world networks compared with the state-of-the-art methods show that, the new model obtains significant improvement for community detection by incorporating node contents effectively.

Haihe River is one of the 7 largest rivers in China. The problem of water pollution in Haihe River Basin is serious. The water quality of Haihe River Basin is generally the best in the north and the worst in the south. And the water quality of the Luanhe River is the best, the proportion of I - III is about 60%, and the water quality of the Tuhaimajia River is the worst, the proportion of >V exceeds 60%. According to the trend of water quality change, the improvement of Shandong Province in Tuhaimajia River system is the most obvious, and proportion of >V water decreased from 100% to about 30%.

Representation learning is believed to produce high-level representations of underlying dynamics in temporal sequences. A three-dimensional convolutional neural network trained to predict future frames in ultrasound tongue and optical lip images creates features for a continuous hidden Markov model based speech recognition system. Predictive tongue features are found to generate lower word error rates than those obtained from an auto-encoder without future frames, or from discrete cosine transforms. Improvement is apparent for the monophone/triphone Gaussian mixture model and deep neural network acoustic models. When tongue and lip modalities are combined, the advantage of the predictive features is reduced.

Community detection is an important data analysis problem in many different areas, and how to enhance the quality of community detection in complicated real applications is still a challenge. Current community detection enhancement methods often take the enhancement as a preprocess of community detection. They mainly focus on how to design the suitable topological similarity of nodes to adjust the original network, but did not consider how to make use of this topological similarity more effectively. In order to better utilize the similarity information, we propose a model framework which integrates the enhancement into the whole community detection procedure. First, we calculate the structural similarity of nodes based on network topology. Second, we present a stochastic model to describe the community memberships of nodes; we then model the strong constraint based on structural similarity, i.e., we make each node have the same community membership distribution with its most similar neighbors; and then we model the weak constraint, i.e., if two nodes have a high similarity we will make their community membership distributions close, otherwise we will make them not close. Finally, we present a nonnegative matrix factorization approach to learn the model parameters. We evaluate our method on both synthetic and real-world networks with ground-truths, and compare it with five comparable methods. The experimental results demonstrate the superior performance of our new method over the competing ones for community detection and enhancement.

Network embedding, aiming at learning the low-dimensional representations of nodes in a network, is a key to many network analysis tasks. All the current network embedding methods primarily explore the network topology or node attributes, while no effort has been made to analyze the edge content for network embedding. The edge content, such as the email content between two users in an email network, is often naturally associated with edges. They carry rich information to describe the interaction between nodes, and provide valuable supervision to learn the representations of nodes. In this paper, we propose a novel edge content enhanced network embedding model, which incorporates the edge content to guide the network representation learning process. We provide the efficient updating rules to infer the parameters in the model, along with theoretical analysis on correctness and convergence guarantees. Extensive experiments, in comparison with the state-of-the-arts, show the superior performance of our proposed new approach on different network analysis tasks.

The audio data is always continually increasing in many real applications. Recent works have shown that there was an underlying manifold on which speaker utterances live in the model-parameter space. However, the existing speaker detection methods did not consider both these issues together, which limits their real applications. For this problem, we propose an incremental speaker identification method which makes use of manifold structure. First, we assume there is a network of speeches which already labeled its communities (speakers). For a new coming speech data, we make an initial decision to assign it to the community which includes most of its (k) neighbors within a local (epsilon) neighborhood space, and then update the speech network. After a batch of data, we refine the current structure using label propagation, i.e., we make each node belong to the community on the condition that most of its neighbors belonging to. Using the incremental as well as local determination-based strategy, our method can not only efficiently process the streaming speech data, but also describe the underlying manifold structure of the speech data. Some experiments were conducted on a large Chinese speaker recognition data, and the results confirmed the effectiveness of our new approach.

Zinc is an essential metal in bacteria. One important bacterial zinc transporter is AdcA, and most bacteria possess AdcA homologs that are single-domain small proteins due to better efficiency of protein biogenesis. However, a double-domain AdcA with two zinc-binding sites is significantly overrepresented in Streptococcus species, many of which are major human pathogens. Using molecular simulation and experimental validations of AdcA from Streptococcus pyogenes, we found here that the two AdcA domains sequentially stabilize the structure upon zinc binding, indicating an organization required for both increased zinc affinity and transfer speed. This structural organization appears to endow Streptococcus species with distinct advantages in zinc-depleted environments, which would not be achieved by each single AdcA domain alone. This enhanced zinc transport mechanism sheds light on the significance of the evolution of the AdcA domain fusion, provides new insights into double-domain transporter proteins with two binding sites for the same ion, and indicates a potential target of antimicrobial drugs against pathogenic Streptococcus species.

This paper addresses the problem of automatic optical phase identification of micro-drill bits for micro-drilling tool inspection in printed circuit board production. To overcome the limitations of conventional active shape model (ASM) on shape modeling of micro-drill bits, six key landmarks are defined for the initialization and optimization of ASM, and a novel method based on projection profiles is also proposed for these key landmarks detection. In addition, to involve the local shape feature, a bag of shape segment (BoSS) model is developed. Based on the improved ASM and BoSS, a new shape representation of micro-drill bits is proposed for phase identification. Experimental results show that the proposed method outperforms the conventional ASM and can improve the phase identification accuracy of micro-drill bits.

Feature extraction of speaker information from speech signals is a key procedure for exploring individual speaker characteristics and also the most critical part in a speaker recognition system, which needs to preserve individual information while attenuating linguistic information. However, it is difficult to separate individual from linguistic information in a given utterance. For this reason, we investigated a number of potential effects on speaker individual information that arise from differences in articulation due to speaker-specific morphology of the speech organs, comparing English, Chinese and Korean. We found that voiced and unvoiced phonemes have different frequency distributions in speaker information and these effects are consistent across the three languages, while the effect of nasal sounds on speaker individuality is language dependent. Because these differences are confounded with speaker individual information, feature extraction is negatively affected. Accordingly, a new feature extraction method is proposed to more accurately detect speaker individual information by suppressing phoneme-related effects, where the phoneme alignment is required once in constructing a filter bank for phoneme effect suppression, but is not necessary in processing feature extraction. The proposed method was evaluated by implementing it in GMM speaker models for speaker identification experiments. It is shown that the proposed approach outperformed both Mel Frequency Cepstrum Coefficient (MFCC) and the traditional F-ratio (FFCC). The use of the proposed feature has reduced recognition errors by 32.1-67.3% for the three languages compared with MFCC, and by 6.6-31% compared with FFCC. When combining an automatic phoneme aligner with the proposed method, the result demonstrated that the proposed method can detect speaker individuality with about the same accuracy as that based on manual phoneme alignment.

Deep neural network (DNN) acoustic models have significantly advanced forward in recent years, outperforming the traditional Gaussian Mixture Hidden Markov Model (GMM-HMM) in large vocabulary continuous speech recognition tasks. We try to develop a practical Lhasa Tibetan ASR system. For higher speech recognition accuracy, in this paper, we consider to investigate the performances of Tibetan acoustic modeling using DNN method based on several different phoneme sets, which are defined based on linguistic and phonological knowledge of Tibetan Lhasa dialect. Experiments are conducted on a Tibetan corpus recorded by 20 persons, using a bigram language model over phones. The phone error rate (PER) results show that the acoustic model with CTL set performs best, which is relatively 10.43% higher accuracy than the basic phoneme set. Moreover, our results confirm the fact that for Lhasa Tibetan acoustic model, the paradigm DNN-HMM outperforms the conventional GMM-HMM.

Ultrasound imaging of the tongue has been used for decades in studies of speech production and speech motor control, for silent speech interfaces, and in numerous other areas. Despite substantial efforts, however, extraction of reliable features from ultrasound tongue data remains a challenge due to speckle noise and acoustic propagation issues. Recently, Representation Learning has emerged in a variety of fields as a powerful means of generating useful representations of underlying structure in raw, high-dimensional data. In its unsupervised form, Representation Learning discovers structures in unlabelled data, thereby eliminating the need for a time-consuming labelling step. The present work is believed to be the first use of unsupervised Representation Learning to reveal structures related to tongue dynamics in unlabelled ultrasound video. A 3-D Convolutional Neural Network examining a series of unlabelled 60 Hz tongue images is found to accurately predict unseen future images even for large interframe tongue displacements. By comparing the 3DCNN prediction error to that of a simple previous-frame predictor, tongue trajectories containing transitions between regions of acoustic stability can be identified and correlated with formant trajectories in a spectrogram. Prospects for leveraging the tongue dynamic representation for use in subsequent speech processing tasks will be discussed.Ultrasound imaging of the tongue has been used for decades in studies of speech production and speech motor control, for silent speech interfaces, and in numerous other areas. Despite substantial efforts, however, extraction of reliable features from ultrasound tongue data remains a challenge due to speckle noise and acoustic propagation issues. Recently, Representation Learning has emerged in a variety of fields as a powerful means of generating useful representations of underlying structure in raw, high-dimensional data. In its unsupervised form, Representation Learning discovers structures in unlabelled data, thereby eliminating the need for a time-consuming labelling step. The present work is believed to be the first use of unsupervised Representation Learning to reveal structures related to tongue dynamics in unlabelled ultrasound video. A 3-D Convolutional Neural Network examining a series of unlabelled 60 Hz tongue images is found to accurately predict unseen future images even for large interframe...

The interaction between vincamine (VCM) and human serum albumin (HSA) has been studied using a fluorescence quenching technique in combination with UV/vis absorption spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, circular dichroism (CD) spectroscopy and molecular modeling under conditions similar to human physiological conditions. VCM effectively quenched the intrinsic fluorescence of HSA via static quenching. The binding constants were calculated from the fluorescence data. Thermodynamic analysis by Van't Hoff equation revealed enthalpy change (ΔH) and entropy change (ΔS) were -4.57 kJ/mol and 76.26 J/mol/K, respectively, which indicated that the binding process was spontaneous and the hydrophobic interaction was the predominant force. The distance r between the donor (HSA) and acceptor (VCM) was obtained according to the Forster's theory of non-radiative energy transfer and found to be 4.41 nm. Metal ions, viz., Na(+), K(+), Li(+), Ni(2+), Ca(2+), Zn(2+) and Al(3+) were found to influence binding of the drug to protein. The 3D fluorescence, FT-IR and CD spectral results revealed changes in the secondary structure of the protein upon interaction with VCM. Furthermore, molecular modeling indicated that VCM could bind to the subdomain IIA (site I) of HSA.

Detailed analysis of tonal features for Tibetan Lhasa dialect is an important task for Tibetan automatic speech recognition (ASR) applications. However, it is difficult to utilize tonal information because it remains controversial how many tonal patterns the Lhasa dialect has. Therefore, few studies have focused on modeling the tonal information of the Lhasa dialect for speech recognition purpose. For this reason, we investigated influences of the tonal information on the performance of Lhasa Tibetan speech recognition. Since Lhasa Tibetan has no conclusive tonal pattern yet, in this study, we used a four-tone pattern and designed a phone set based on the four contour contrasts scheme. Speech recognition performance was examined using the acoustic model with and without the pitch-related features. The experimental results showed that the character error rate (CER) was improved 11% after applying the tone based phone set and pitch-related features to DNN-HMM based speech recognition by comparing to that without tonal information. This preliminary study revealed that the tonal information plays an important role in speech recognition of Tibetan Lhasa dialect.

In this paper, an automatic visual inspection scheme with phase identification of microdrill bits in printed circuit board (PCB) production is proposed. Different from conventional methods in which the geometric quantities of microdrill bits are measured to compare with the prior standards, the proposed method adopts a strategy of machine learning. Thus, it lowers the requirement for the enlargement of lens and the resolution of charge-coupled device; therefore, the cost of inspecting instrument can be relatively reduced. Our method mainly includes two procedures: First, the statistical shape models of microdrill bit are built to get the shape subspace, and then the phase identification is performed in the shape subspace using some pattern recognition techniques. In this paper, we compared the performance of two statistical model methods, principal component analysis (PCA) and linear discriminate analysis, together with three classifiers, support vector machines (SVMs), neural networks, and k-nearest neighbors, respectively, for phase identification of microdrill bits. The experimental results demonstrate that using low enlargement and resolution microdrill bit images the proposed method can measure up to high inspection accuracy, and provide a conclusion that the highest identification rates are obtained by PCA-SVMs, which are higher than that of the conventional method.

Higher fullerenes (C84, C90, C92, C94 and C96) were successfully isolated from the Soxhlet extract of the synthetical “Graphote Smokes” soot (GS sample) by using a big Cosmosil Buckysep (Phenomenex) column (250 mm × 10 mm) with a large injection. The fractions isolated have been determined by high performance liquid chromatography (HPLC) and laser desorption mass spectrometry (LDMS). It is found that there are different fullerenes molecules in different fractions with retention time. The result indicates that fullerenes do exist in GS samples. Also, it excludes the suspicion to some extent that fullerene molecules might be generated by the laser desorption process in the LDMS. In addition, it also provides the experimental basis for the study of natural higher fullerenes and might be helpful to figure out the question if higher fullerenes do exist in the natural samples.

The interaction between cyproheptadine hydrochloride (CYP) and human serum albumin (HSA) was investigated by fluorescence spectroscopy, UV-vis absorption spectroscopy, Fourier transform infrared spectroscopy (FT-IR) and molecular modeling at a physiological pH (7.40). Fluorescence of HSA was quenched remarkably by CYP and the quenching mechanism was considered as static quenching since it formed a complex. The association constants Ka and number of binding sites n were calculated at different temperatures. According to Förster's theory of non-radiation energy transfer, the distance r between donor (human serum albumin) and acceptor (cyproheptadine hydrochloride) was obtained. The effect of common ions on the binding constant was also investigated. The effect of CYP on the conformation of HSA was analyzed using FT-IR, synchronous fluorescence spectroscopy and 3D fluorescence spectra. The thermodynamic parameters ΔH and ΔS were calculated to be -14.37 kJ mol(-1) and 38.03 J mol(-1) K(-1), respectively, which suggested that hydrophobic forces played a major role in stabilizing the HSA-CYP complex. In addition, examination of molecular modeling indicated that CYP could bind to site I of HSA and that hydrophobic interaction was the major acting force, which was in agreement with binding mode studies.