Conventional Dictionary Research Articles

Social Media and Real Estate: Do Twitter Users Predict REIT Performance?

This study investigates the impact of social media sentiment on indirect real estate market returns by utilizing a comprehensive natural language processing approach to identify relevant Twitter posts and extract sentiment from them. To handle the complex linguistic features inherent in social media messages, three different sentiment classifiers are compared. The findings suggest a significant relationship between monthly sentiment and REIT returns, which occurs in two phases: a short-term speculative reaction and a greater longer-term reaction related to actual changes in the real estate market. The study also highlights that while the conventional dictionary approach can identify this relationship, more sophisticated classifiers can achieve higher accuracy. Overall, the results demonstrate the valuable insights that can be gained from analyzing social media data and its potential impact on the real estate market.

Exploiting sparsity of hyperspectral image: A novel approach for compressive hyperspectral image reconstruction using deep learning

Compressive hyperspectral imaging is an emerging technology that captures compressed two-dimensional (2D) measurements and subsequently reconstructs three-dimensional (3D) hyperspectral images, capitalizing on the sparsity prior. Numerous end-to-end networks have been proposed, yielding notable improvements in reconstruction speed and quality compared to traditional algorithms. However, these networks fall short in exploiting the sparsity of hyperspectral images in their direct mapping from 2D measurements to 3D hyperspectral images. To address this limitation, this paper introduces a novel approach for compressive hyperspectral image reconstruction using deep learning. The deep learning network comprises two components: the sparse coefficient recovery net and the dictionary training net. The dictionary training net incorporates a pre-trained dictionary, whose parameters can also be updated during the backpropagation process. Concurrently, the sparse coefficient recovery net generates sparse coefficients under this dictionary. Our approach draws inspiration from conventional dictionary learning algorithms, enabling effective utilization of signal sparsity. In comparison to several state-of-the-art methods, our method achieves superior reconstruction quality and demonstrates enhanced robustness against noise. The proposed approach is envisioned to offer fresh insights into hyperspectral images reconstruction using deep learning models.

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

The conventional dictionary learning (DL) algorithms aim to adapt the dictionary/sparse code to individual functional magnetic resonance imaging (fMRI) data. Thus, lacking the capability to consolidate the spatiotemporal diversities offered by other subjects. Considering that subject-wise (sw) data matrix can be decomposed into the sparse linear combination of multi-subject (MS) time courses and MS spatial maps, two new algorithms, sw sequential DL (swsDL) and sw block DL (swbDL), have been proposed. They are based on the novel framework, defined by the mixing model, where base matrices prepared by operating a computationally fast sparse spatiotemporal blind source separation method over multiple subjects are employed to adapt the mixing matrices to sw training data. They solve the optimization models formulated using l_0/l_1-norm penalization/constraints through dictionary/sparse code pair update and alternating minimization approach. They are unique because no existing sparse DL method can incorporate MS spatiotemporal components while updating sw atoms/sparse codes, which can eventually be assembled using neuroscience knowledge to extract group-level dynamics. Various fMRI datasets are used to evaluate and compare the performance of the proposed algorithms with existing state-of-the-art algorithms. Specifically, overall, a 14% increase in the mean correlation value and 39% reduction in the mean computation time exhibited by swsDL and swbDL, respectively, over the adaptive consistent sequential dictionary algorithm.

Hydra: competing convolutional kernels for fast and accurate time series classification

We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely Rocket and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling Rocket. We present Hydra, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both Rocket and conventional dictionary methods. Hydra is faster and more accurate than the most accurate existing dictionary methods, achieving similar accuracy to several of the most accurate current methods for time series classification. Hydra can also be combined with Rocket and its variants to significantly improve the accuracy of these methods.

Row-Column Overcomplete Structured Dictionary Learning for Enhanced Fault Detection and Isolation

To improve the monitoring performance of dictionary learning based methods, this article proposes a row-column overcomplete structured dictionary learning (RCOSDL) method for fault detection and isolation of industrial processes. Unlike conventional dictionary learning approaches which are overcomplete column-wise, the proposed method involves a dictionary that is overcomplete both row- and column-wise. The introduction of row-column overcomplete dictionary results in monitoring statistics that are more sensitive to incipient faults. In order to incorporate structured information, two graph Laplacian regularization terms, namely, manifold graph Laplacian and full graph Laplacian are considered. Whilst the inherent local geometric structure in the data is preserved in the sparse representation by the manifold graph Laplacian term, the correlation structure between process variables is preserved by using the full graph Laplacian term. Hence violation in the geometric or correlation structure will be promptly detected. To pinpoint faulty variables, a fault isolation method is developed by imposing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> / <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> -norm constraint on the sparse coefficients. In addition, theoretical property involving the condition for guaranteed fault isolation is presented in Theorem 1. The contributions of this article include the introduction of monitoring statistics based on RCOSDL that are suitable for incipient faults, a new fault isolation scheme as well as theoretical analysis on the condition of guaranteed fault isolation. The better performance of the proposed method is illustrated by applications to numerical studies and practical industrial cases.

Perancangan Aplikasi Kamus Bahasa Batak Menggunakan Metode Searching

In modern times like today, of course there are many dictionaries in circulation, ranging from books, desktop, web, and even mobile. And each has its advantages and disadvantages. For example, a conventional dictionary in the form of a book, has advantages in terms of the amount of vocabulary but requires a long time in terms of Word Search, and less practical to carry anywhere. To overcome this, a mobile-based dictionary is the answer. The author designed the batak language dictionary application with the hope of answering the expectations of users who need a practical and responsive dictionary. The dictionary has not been designed using the searching method. Every country even in a narrower scope, today the development of technology has produced several devices, one form of application. The application is a device that can run several features such as a communication medium that is easy to use and easy to understand so that if there are people who want to learn it will be very easy to understand and for people, especially batak blood also if they do not understand their own language by using this application will understand where the shortcomings in the batak language.

Wavefront reconstruction method for aero-optical distortion based on compressed sensing.

The wavefront sensor plays an important role in the adaptive optics (AO) system for aero-optical distortion correction. However, the bandwidth of the current data interfaces of wavefront sensors, as one of the key factors, limits applications of the AO system in extremely high-frequency aero-optical distortion correction, leading to unsatisfactory performance. In this paper, a framework for wavefront data compression using compressed sensing is established to improve the correction ability of the AO system, and a disturbed Zernike gradient dictionary (DZGD) learning over the k-singular value decomposition algorithm is proposed for achieving good performance in the compression of aero-optical wavefront data. Based on the proposed DZGD, a method for aero-optical distortion data compression and wavefront reconstruction is developed that can efficiently reduce the amount of data in the information channel without degradation of the correction effect in aero-optical distortion correction. The compressibility of aero-optical distortions over the DZGD is analyzed in detail by numerical simulations. In addition, the selection criteria of the measurement matrix and the anti-noise characteristic of the method are also discussed. Data compression using our method is feasible and highly adaptable in the correction of aero-optical distortions, and exhibits stronger resistance against detector noise compared with using the conventional dictionary.

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation, which aims to recover each source's signal using its corresponding sub-dictionary, has attracted many scholars' attention. The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source, and this information can be used to recover almost every sample from that source. However, in a more general sense, the samples from a source are composed not only of discriminative information but also common information shared with other sources. This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance. The innovations are threefold. Firstly, an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary. Secondly, a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source. Thirdly, a source separation scheme based on the learned dictionary is presented. Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Deep Learning Based Downlink Channel Covariance Estimation for FDD Massive MIMO Systems

Obtaining the downlink channel state information in frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems is challenging due to the overwhelming training and feedback overhead. In this letter, motivated by the existence of mapping characteristics between uplink and downlink, we propose a covariance variational auto-encoder network (CVENet) to approximate the mapping function. Different from normal auto-encoder, the CVENet extracts the uplink channel covariance to a latent distribution space and then predicts the downlink channel covariance by the sample of the space. Simulation results demonstrate that the CVENet performs better than the conventional dictionary pairs algorithm. And the CVENet still achieves robustness in a circumstance where the channel environment of the training stage is different from the deployment stage, which shows its practical applicability.

Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval

Due to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary.

Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with non-convex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms.

Hardness-Aware Dictionary Learning: Boosting Dictionary for Recognition

Sparse representation is a powerful tool in many visual applications since images can be represented effectively and efficiently with a dictionary. Conventional dictionary learning methods usually treat each training sample equally, which would lead to the degradation of recognition performance when the samples from same category distribute dispersedly. This is because the dictionary focuses more on easy samples (known as highly clustered samples), and those hard samples (known as widely distributed samples) are easily ignored. As a result, the test samples which exhibit high dissimilarities to most of intra-category samples tend to be misclassified. To circumvent this issue, this paper proposes a simple and effective hardness-aware dictionary learning (HADL) method, which considers training samples discriminatively based on the AdaBoost mechanism. Different from learning one optimal dictionary, HADL learns a set of dictionaries and corresponding sub-classifiers jointly in an iterative fashion. In each iteration, HADL learns a dictionary and a sub-classifier, and updates the weights based on the classification errors given by current sub-classifier. Those correctly classified samples are assigned with small weights while those incorrectly classified samples are assigned with large weights. Through the iterated learning procedure, the hard samples are associated with different dictionaries. Finally, HADL combines the learned sub-classifiers linearly to form a strong classifier, which improves the overall recognition accuracy effectively. Experiments on well-known benchmarks show that HADL achieves promising classification results.

Development of fast deep learning quantification for magnetic resonance fingerprinting in vivo

PurposeA deep neural network was developed for magnetic resonance fingerprinting (MRF) quantification. This study aimed at extending previous studies of deep learning MRF to in vivo applications, allowing sub-second computation time for large-scale data. MethodsWe applied the deep learning methodology based on our previously published multi-layer perceptron. The number of layers was four, which was optimized to balance the model capacity and noise robustness. The training sets were obtained from MRF dictionaries with 9000 to 28,000 atoms, depending on the desired T1 and T2 ranges. The simulated MRF undersampling artifact based on the k-space acquisition scheme and noise were both added to the training data to reduce the error in estimates. ResultsThe neural network achieved high fidelity (R2 _ 0.98) as compared to the T1 and T2 values of the ISMRM standardized phantom. In brain MRF experiment, the model trained with simulated artifacts and noise showed less error compared to that without. The in vivo application of our neural network for liver and prostate were also demonstrated. For an MRF slice with 256 _ 256 image resolution, the computation time of our neural network was 0.12 s, compared with the _ 28 s-pre-slice for the conventional dictionary matching method. ConclusionOur neural network achieved fast computation speed for MRF quantification. The model trained with simulated artifacts and noise showed less error and achieved optimal performance for phantom experiment and in vivo normal brain and liver, and prostate cancer patient.

Class-Specific Sparse Principal Component Analysis for Visual Classification

Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the reducing reconstruction error and enhancing the representative information. In this paper, we focus on designing capable dictionary learning architecture for the visual classification task with few-shot training samples. First, we propose a class-specific sparse PCA approach to extend the conventional dictionary learning to dictionary pair learning for visual classification. The orthogonal synthesis dictionary ensures fewer reconstruction errors with fixed dictionary size while the sparse analysis dictionary guarantees the representative features. Second, we develop the Alternating Direction Method of Multipliers algorithm to optimize the analysis dictionary to improve computational efficiency. Extensive experiments conducted on multiple image datasets demonstrate that the proposed class-specific PCA method performs favorably against the state-of-art methods on eight widely used image datasets.

Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model

Seismic deconvolution is a typical ill-posed inverse problem. The regularization technique in terms of different prior information is used for a unique and stable solution. Due to the difference between prior information and the actual subsurface situation, it is hard to obtain a solution with satisfactory accuracy and resolution. This letter presents the dictionary learning as an efficient adaptive deconvolution method for the reflectivity reconstruction problem. Considering the curse of dimensionality of conventional dictionary learning and the suboptimal solution of the patch-based dictionary learning, we take the convolutional sparse coding (CSC) model as the dictionary learning method. In this method, the prior information can be obtained from the well-log data in the form of sparse CSC dictionary of reflectivity. On the assumption that the deposition of the subsurface layers is stable, the CSC dictionary extracted from the well-log data can also be applied in the whole work area. The CSC-based deconvolution can be seen as the adaptive deconvolution due to the independence of the assumption made about the reflectivity and seismic data. The process of the adaptive CSC-based deconvolution is divided into three parts. First, the CSC dictionary is learned from the well-log data. Then, the objective function is formulated by combining the CSC dictionary and the single-channel seismic record misfit term for the reconstruction of reflectivity. Finally, the objective function is efficiently solved with the coordinate descent approach. We illustrate the performance of our adaptive deconvolution with synthetic and field seismic data.

Multiple vector representations of images and robust dictionary learning

In this paper novel multiple vector representations of images are proposed and a robust dictionary learning method is designed. The multiple vector representation scheme enables an image to be observed with multiple views. Moreover, multiple vector representations are directly generated from the original image via a simple and efficient way whereas multi-view data usually have a high acquiring cost. The proposed method applies the same dictionary learning algorithm to the multiple vector representations and designs a very reasonable weighted logarithmic sum scheme to integrate classification scores of all vector representations. Main merits of this work are in the following points. First, it offers a quite novel viewpoint to take insight into representation of objects. It for the first time reveals that rows and columns of images can be viewed as two different sequences and pixel arrangements in terms of rows and columns of the image allow the object to be observed with two different angles of view. Second, it shows that when conventional dictionary learning algorithms are combined with the proposed multiple vector representations and weighted logarithmic sum scheme, very robust and accurate classification results can be obtained. This also partially means that diversity of vector representations of the image can be further consolidated by matrix decomposition in dictionary learning, so the resultant complementary information can be better exploited. Third, differing from conventional research routines, our study tells us that to fully dig and exploit possible representation diversity might be a better way to lead to potential various appearance and high classification accuracy of the image. The code of the proposed method is accessible at http://www.yongxu.org/lunwen.html.

Analysis Dictionary Learning Based Classification: Structure for Robustness.

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated function to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large-scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.

Measuring China's Stock Market Sentiment

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word lists and supervised machine-learning methods (support vector machine and convolutional neural network). The market-level textual sentiment index is constructed as the average of message-level sentiment scores, and the textual disagreement index is constructed as their dispersion. These textual measures allow us to test a range of predictions of classical behavioral asset-pricing models within a unified empirical setting. We find that textual sentiment can significantly predict market return, exhibiting a salient underreaction-overreaction pattern on a time scale of several months. This effect is more pronounced for small and growth stocks, and is stronger under higher investor attention and during more volatile periods. We also find that textual sentiment exerts a significant and asymmetric impact on future volatility. Finally, we show that trading volume will be higher when textual sentiment is unusually high or low and when there are more differences of opinion, as measured by our textual disagreement. Based on a massive textual dataset, our analysis provides support for the noise-trading theory and the limits-to-arbitrage argument, as well as predictions from limited-attention and disagreement models.

Multi-resolution dictionary learning for face recognition

In recent years, there has been a growing interest in the study of dictionary learning for face recognition. Most of the conventional dictionary learning methods focus only on a single resolution, which ignores the variability of resolutions of real-world face images. In order to address the above issue, this paper proposes a novel multi-resolution dictionary learning method that provides multiple dictionaries each being associated with a resolution. Especially, to enhance the robustness of the model, our method adds a relatively strong constraint to keep the similarity of representations obtained using different dictionaries in the training phase. We compare the proposed method to several state-of-the-art dictionary learning methods by applying this method to multi-resolution face recognition. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning methods. The MATLAB codes of the proposed method will be available at http://www.yongxu.org/lunwen.html.

Perancangan &amp; Implementasi Aplikasi Kamus Teknik Sipil Berbasis Android Dengan Menggunakan Eclipse Juno

This study aims to produce an Android-based Civil Engineering dictionary application with digitize a conventional Civil Engineering dictionary into a digital dictionary, because currently there are many Android users. This research is motivated by the lack of search for the term Civil Engineering because it still uses conventional books and takes a long time. Android-based Civil Engineering dictionary application is built with the Java programming language, and Eclipse Juno as an editor to edit program code and sqlite as a database. The system development method used in this research is Rapid Application Development (RAD) method. This method shows that the product has a faster life cycle and gets better results, before the product is implemented, the product is tested. Based on the results of the expert review showed an average value of 4.23 with very valid criteria and also tested with users, namely Civil Engineering students with an average value of 4.5 with very practical criteria so that the dictionary application of the term Android-based Civil Engineering is worthy of use and can assist students and the community in finding the meaning of the term Civil Engineering quickly and efficiently