High-dimensional Information Research Articles

Case-Based Reasoning and Attribute Features Mining for Posting-Popularity Prediction: A Case Study in the Online Automobile Community

Social media is in a dynamic environment of real-time interaction, and users generate overwhelming and high-dimensional information at all times. A new case-based reasoning (CBR) method combined with attribute features mining for posting-popularity prediction in online communities is explored from the perspective of imitating human knowledge reasoning in artificial intelligence. To improve the quality of algorithms for CBR approach retrieval and extraction and describe high-dimensional network information in the form of the CBR case, the idea of intrinsically interpretable attribute features is proposed. Based on the theory and research of the social network combined with computer technology of data analysis and text mining, useful information could be successfully collected from massive network information, from which the simple information features and covered information features are summarized and extracted to explain the popularity of the online automobile community. We convert complex network information into a set of interpretable attribute features of different data types and construct the CBR approach presentation system of network postings. Moreover, this paper constructs the network posting cases database suitable for the social media network environment. To deal with extreme situations caused by network application scenarios, trimming suggestions and methods for similar posting cases of the network community have been provided. The case study shows that the developed posting popularity prediction method is suitable for the complex social network environment and can effectively support decision makers to fully use the experience and knowledge of historical cases and find an excellent solution to forecasting popularity in the network community.

C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

Research on Fingerprint and Hyperbolic Fusion Positioning Algorithm Based on 5G Technology

With the development of the Internet of Things technology, higher requirements are put forward for the positioning accuracy of objects. This paper presents an indoor fusion positioning algorithm based on the 5th Generation Mobile Communication Technology (5G), which effectively solves two problems. The first is that fingerprint positioning is susceptible to environmental changes and results in inaccurate fingerprint matching. The second is the problem of the hyperbolic positioning algorithm based on the line-of-sight fluctuating too much in complex indoor environments. This paper uses a 5G flexible subcarrier interval of Orthogonal Frequency Division Multiplexing (OFDM) to significantly reduce the time delay error of Time Of Arrival (TOA), and an improved genetic algorithm and the weighted hyperbolic algorithm are used to estimate the optimal position coordinates. In the offline database establishment stage of fingerprint positioning, the Channel State Information-reference signal (CSI-RS) of multiple-beam sets provides high-dimensional information for subsequent training and prediction. The online stage cooperates with the improved residual network model to make predictions. Finally, the positioning information and the error distribution function generated by the two positioning processes are simultaneously used as the input of the Kalman filter to obtain the precise position coordinates. The simulation results show that in complex indoor scenes where line-of-sight propagation and non-line-of-sight propagation paths are mixed, the accuracy of this method can reach below 0.82 m. Thus, the positioning accuracy is significantly improved compared with other methods, which can meet most indoor scene positioning needs.

Unsupervised manifold learning with polynomial mapping on symmetric positive definite matrices

In this paper, an unsupervised manifold learning algorithm with polynomial mapping on the symmetric positive-definite (SPD) matrix manifold is introduced by matrix information geometry method for data dimensional reduction. Firstly, the mathematical knowledge about the SPD matrix manifold is presented including the metric, geodesic and submanifold. And then, the high dimensional information coordinates are given by different SPD matrix data for constructing the polynomial kernel matrix, weight matrix and sparsity preserving matrix. Next, the manifold learning algorithm on the SPD matrix manifold is proposed by polynomial mapping with geodesic distance. Finally, comparing with some conventional methods in terms of accuracy rate and time cost, the preliminary analysis results indicate that the proposed approach is able to offer a consistent and comprehensive method to realize the SPD matrix data dimensional reduction.

Nonlinear manipulation of orbital angular momentum spectra with second- and third-harmonic generation in a quasi-periodically poled crystal

Optical orbital angular momentum (OAM), as an important degree of freedom of light, has been attracting extensive attention due to its intrinsic feature of the natural discrete infinite dimension. Manipulation of OAM spectra is crucial for many impressive applications from classical to quantum realms, in particular, nonlinear manipulation of OAM spectra. Here, we realized the nonlinear manipulation of OAM spectra by using the simultaneous second- and third-harmonic generation in a single nonlinear crystal of quasi-periodically poled potassium titanyl phosphate, for fundamental waves with a variety of OAM spectra, especially for customized OAM spectra of the second and third harmonics. The experimental results confirmed the theoretical predictions. Our approach not only provides a way to manipulate OAM spectra at shorter wavelengths that are hard to be directly generated but also may find applications toward multiplexing in classical optics and high-dimensional information processing in quantum optics.

A multi-label feature selection method based on an approximation of interaction information

High-dimensional multi-label data is widespread in practical applications, which brings great challenges to the research field of pattern recognition and machine learning. Many feature selection algorithms have been proposed in recent years, among which the filtering feature selection algorithm is the most popular one because of its simplicity. Therefore, filtering feature selection has become a hot research topic, especially the multi-label feature selection algorithm based on mutual information. In the algorithm, the computation cost of high dimensional mutual information is expensive. How to approximate high order mutual information based on low order mutual information has become a major research direction. To our best knowledge, all existing feature selection algorithms that consider the label correlation will increase the computational cost greatly. Therefore, this paper proposes an approximation method of three-dimensional interaction information, which is applied to the calculation of correlation and redundancy. It can take the correlation of labels into account and don’t increase the computation cost significantly at the same time. Experiments analysis results show that the proposed method is effective.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Peripheral lipidomics analyses with ensemble machine learning predict response to neoadjuvant therapy in breast cancer.

582 Background: Neoadjuvant therapy (NAT) is critical in the therapeutic strategy for locally advanced breast cancer (BC) patients. Biomarkers to predict the pathologic complete response (pCR) and to screen beneficial responders need to be urgently explored. Lipidomics is a high-throughput analysis technique, which has potential applications for peripheral biomarkers’ detection to overcome the difficulty in serial pathological biopsies and tumor heterogeneity. Machine learning is a method for mining high-dimensional information with high group and low intermolecular correlation. Here we construct multidimensional models with machine learning approach combining clinical clinicopathological and lipidomics data to predict NAT response. Methods: Plasma samples were collected from 119 BC patients before after two cycles of NAT. Peripheral lipidomic profiles at multiple levels of lipid composition, concentration, chain length, and saturation were dynamically monitored by using absolute quantitative lipidomics. Responders (pCR) and non-responders (non-pCR) groups were randomly sampled in a 1:1.5 ratio for subsequent analysis. Screening of candidate lipidomic biomarkers were performed according to the criteria “VIP>1, FC>2 or FC<0.5 and p<0.05”. Above feature screening were validated by using ensemble machine learning algorithms (Logistic regression, Random forest and Support vector machine). Pearson correlation coefficients were calculated for the expressions of candidate biomarkers. Multidimensional prediction models with the logistic regression algorithm were constructed by correlating candidate lipidomics features with clinicopathologic phenotypes prospectively collected with baseline and dynamic changes. The performance of the models constructed above were assessed by ROC analysis. Results: A total of 8 major classes, 39 subclasses, and 2292 molecules of lipid metabolites were identified in the 235 plasma samples. Most of the candidate biomarkers had lower correlations, indicating lower overlapping and more optimal combination of panels. The prediction models constructed by baseline correlating candidate lipidomics features with ensemble machine learning achieved an Area under the Curve (AUC) of 0.84 for the training set and 0.72 for the validation set with the accuracy 0.84, the specificity 0.92, and the sensitivity 0.71 when the cutoff value is 0.57.The prediction models constructed with lipidomics dynamic changes also achieved good performance. Conclusions: The study suggested a possibility that peripheral lipidomics provided a potential tool to develop multidimensional models with ensemble machine learning for predicting response to NAT for BC patients with good discrimination power, which might guide individual optimal NAT strategies and avoid unnecessary treatment.

Divergence-degenerate spatial multiplexing towards future ultrahigh capacity, low error-rate optical communications

Spatial mode (de)multiplexing of orbital angular momentum (OAM) beams is a promising solution to address future bandwidth issues, but the rapidly increasing divergence with the mode order severely limits the practically addressable number of OAM modes. Here we present a set of multi-vortex geometric beams (MVGBs) as high-dimensional information carriers for free-space optical communication, by virtue of three independent degrees of freedom (DoFs) including central OAM, sub-beam OAM, and coherent-state phase. The novel modal basis set has high divergence degeneracy, and highly consistent propagation behaviors among all spatial modes, capable of increasing the addressable spatial channels by two orders of magnitude than OAM basis as predicted. We experimentally realize the tri-DoF MVGB mode (de)multiplexing and data transmission by the conjugated modulation method, demonstrating lower error rates caused by center offset and coherent background noise, compared with OAM basis. Our work provides a potentially useful basis for the next generation of large-scale dense data communication.

Fast weighted CP decomposition for context-aware recommendation with explicit and implicit feedback

As an effective method for high-dimensional information processing, tensor factorization has been widely used in context-aware recommendation. However, the multi-dimensional processing of tensor factorization usually leads to more serious data sparsity. Making full use of the data in recommendation systems, including a small amount of explicit rating data and a large amount of implicit feedback data such as clicks, plays, and purchases, can effectively alleviate this problem. Meanwhile, the processing of sparse explicit data and huge implicit feedback data makes tensor factorization face a severe challenge of computational efficiency. Therefore, this study proposes a fast tensor factorization model named FWCP that unifies explicit and implicit feedback information, which mainly studies as follows: (1) Assigning non-uniform weights to observed and unobserved values to unify explicit and implicit feedback information; (2) Memorizing intermediate data which generated by element-wise alternating least squares(ALS), and implementing parallel optimization of FWCP, which can reduce the training time of tensor factorization based on whole-data. The evaluation results on two real datasets showed that FWCP proposed in this study is superior to other context-aware recommendation algorithms.

Simple-Diffraction-Based Deep Learning to Reconstruct a High-Dimensional Orbital-Angular-Momentum Spectrum Via Single-Shot Measurement

Orbital-angular-momentum (OAM) complex-valued spectrum reconstruction routinely necessitates the measurements of a complete set of observables, which is rather time consuming and inefficient for high-dimensional OAM superposition state. Here we break this barrier to realize the full reconstruction of a high-dimensional OAM complex-valued spectrum via only a single-shot measurement, which benefits from the use of a residual convolutional neural network for learning the diffraction behaviors. In our experiment, the high-dimensional OAM states are diffracted by a simple aperture of pentagram that breaks the OAM conjugate symmetry. We achieve fidelity over 97.8% and 92.1% for high-dimensional pure and mixed OAM states (up to $d=15$), respectively. We also confirm the excellent robustness under strong noise environment. Our method will find potential applications in high-dimensional information encoding, such as large-alphabet communications, quantum imaging, and real-time quantum sensing.

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.

Expression Recognition of Classroom Children’s Game Video Based on Improved Convolutional Neural Network

Humans express emotions in many ways, such as gestures, limbs, and expressions. Among them, facial expressions are the most intuitive way to express human inner emotional activities in human-to-human communication. With the rapid development of computer vision, facial expression recognition is an important research topic in the field of computer vision. It plays a key role in nonverbal communication and can be applied to human-computer interaction, social robotics, video games, and other fields. Traditional expression recognition algorithms require complex manual feature extraction, which takes a long time, and the accuracy of expression recognition in complex scenes is not high. However, with the development of deep learning, especially the convolutional neural network, facial expression recognition technology has also developed rapidly, and the recognition accuracy has been greatly improved. This paper studies the facial expression recognition method of classroom children’s game video based on convolutional neural network and proposes a convolutional neural network with deeper layers. The full connection is modified to 4 layers of convolution, 4 layers of pooling, and 2 layers of full connection. Firstly, the facial expression image is preprocessed by, for example, key point location, face cropping, and image normalization; then, the convolutional layer is used to extract the low-dimensional and high-dimensional feature information of the face image; and the pooling layer is used to extract the face image. The feature information is dimensionally reduced. Finally, the softmax classifier is used to classify and recognize the expressions of the training sample images. In order to improve the accuracy of expression recognition, a self-made set of labeled pictures was added to the expression training set. Simulation and comparison experiments show that the improved model has higher accuracy and smoother loss curve, which verifies the effectiveness of the improved network.

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

With the development of modern society, the demand for indoor positioning technology is higher and higher. The existing indoor positioning technology is difficult to really solve the problem of accuracy and achieve high-performance indoor positioning system design. Based on iBeacon equipment, this paper proposes a method to optimize received signal strength indication indoor positioning algorithm by using the Gaussian filtering method so as to reduce the adverse impact of multipath fading in indoor environment. In order to further improve the accuracy of indoor positioning algorithm, the stack automatic encoder in the deep neural network algorithm is introduced. Through the deep learning method, the high-dimensional information of the fingerprint database collected by the system can be extracted and the adverse impact of data noise on the database is also reduced. Through the simulation test of the system, it can be seen that the error of received signal strength indication indoor positioning algorithm based on extended Gaussian filter is small. Compared with the traditional iBeacon algorithm, the improved algorithm can achieve better data classification. The maximum error of the whole system is 1.02 M. Comprehensive analysis shows that the proposed indoor positioning system has a certain practical value and can be applied to the indoor positioning needs in a certain range of environment.

Quantum coherence of an orbital angular momentum multiplexed continuous-variable entangled state

Orbital angular momentum (OAM) multiplexed entangled state is an important quantum resource for high dimensional quantum information processing. In this paper, we experimentally quantify quantum coherence of OAM multiplexed continuous-variable (CV) entangled state and characterize its evolution in a noisy environment. We show that the quantum coherence of the OAM multiplexed CV entangled state carrying topological charges l = 1 and l = 2 are the same as that of the Gaussian mode with l = 0 in a noisy channel. Furthermore, we show that the quantum coherence of OAM multiplexed entangled state is robust to noise, even though the sudden death of entanglement is observed. Our results provide reference for applying quantum coherence of OAM multiplexed CV entangled state in a noisy environment.

A Grey Incidence Based Group Decision-Making Approach and Its Application

We define the group measure matrix of the alternative scheme and the ideal scheme based on the relevant factor sequence and the system characteristic behavior sequence. Furthermore, the information distances of decision makers and decision criteria are defined, respectively. According to the information distance, we obtain each scheme’s grey matrix incidence degree for the scheme ranking. Finally, we use an example to verify the rationality of the model and compare it with other classic methods, such as TOPSIS, VIKOR, MULTI-MOORA. Compared with previous grey incidence analysis model, the proposed model can make full use of information of the decision-maker dimension and the criteria dimension. The proposed model can avoid high-dimensional information loss. The results show that the proposed method has superiority in measuring decision-maker information and decision-making standard information.

A Design of CGK-Based Granular Model Using Hierarchical Structure

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models.

Four-state reference-frame-independent quantum key distribution with non-qubit source flaws

The discrepancy between theory and experiment severely limits the development of quantum key distribution (QKD). Reference-frame-independent (RFI) protocols have been proposed to avoid alignment of the reference frame with the assumption of qubit systems. However, Trojan horse attacks and high-dimensional information leakage caused by equipment loopholes inevitably result in an imperfect non-qubit emitted signal. In this paper, we analyze the security of the RFI-QKD protocol with non-qubit source flaws based on a generalizing loss-tolerant method. The simulation results show that it is feasible to employ non-qubit flaw sources without compromising the security of the QKD system. Moreover, it only requires the preparation of four quantum states, which reduces the complexity of the experiment.

Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Network Embedding Algorithm Taking in Variational Graph AutoEncoder

Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common latent representation space, to encode the high-dimensional sparse network information to the low-dimensional dense vector representation, effectively improving the performance of the network analysis tasks. The current research on attributed network embedding is presently facing problems of high-dimensional sparsity of attribute eigenmatrix and underutilization of attribute information. In this paper, we propose a network embedding algorithm taking in a variational graph autoencoder (NEAT-VGA). This algorithm first pre-processes the attribute features, i.e., the attribute feature learning of the network nodes. Then, the feature learning matrix and the adjacency matrix of the network are fed into the variational graph autoencoder algorithm to obtain the Gaussian distribution of the potential vectors, which more easily generate high-quality node embedding representation vectors. Then, the embedding of the nodes obtained by sampling this Gaussian distribution is reconstructed with structural and attribute losses. The loss function is minimized by iterative training until the low-dimension vector representation, containing network structure information and attribute information of nodes, can be better obtained, and the performance of the algorithm is evaluated by link prediction experimental results.