Updated Dictionary Research Articles

Atom-profile updating dictionary learning with nucleus sampling attention mechanism sparse coding for audio magnetotelluric denoising

Audio magnetotellurics (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of the earth. However, the accuracy of AMT in translating geoelectrical structures decreases when the data collected in mining areas are of poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we develop a new denoising method that combines atom-profile updating dictionary learning (APrU) with the nucleus sampling attention mechanism (NSAM) sparse coding. First, we use APrU to accurately learn the characteristics of the noise in the AMT data; then, we apply the updated dictionary to perform sparse coding on the AMT data by NSAM to obtain the noise; finally, we subtract the noise from the original AMT data to obtain the denoised data. Our experimental results suggest that the proposed method can learn an overcomplete dictionary via the to-be-processed AMT data, thereby enabling the sparse representation of the noise within the learned dictionary. We also demonstrate the efficacy of this method with a set of field data collected from the Lu-Zong mining area, and the attained denoised data faithfully restore the geoelectrical structures with heightened accuracy. The findings confirm that the proposed method realizes the unsupervised learning of the AMT data and allows us to achieve precise denoising performance.

ЛЕКСИЧНІ ПРОБЛЕМИ ПЕРЕКЛАДУ ТЕКСТІВ У СФЕРІ ЕНЕРГОЕФЕКТИВНОСТІ

The article examines the main ways of translating English lexical units in the sphere of energy efficiency using the Ukrainian language. The relevance of the proposed research is substantiated in view of the modern needs of society and the problems of science. Based on the results of modern scientific studies, the essence of the concept of the term has been clarified. The publications devoted to the study of various aspects of the terms were analyzed, and the requirements put forward to this layer of the lexical structure of the language were singled out. It is emphasized that there are no terms that fully meet all requirements and this creates certain difficulties in the translation process. It was found that there are still a number of challenges faced by a translator who works with specialized vocabulary. Among them is the need to deepen knowledge in a certain sphere, consult with experts, lack of updated dictionaries of terms, etc. As a result of the analysis of the received data, it was found that the majority of multi-component terms in the sphere of energy are translated by calquing. The results of our research also indicate the predominance of the multicomponent term system of the energy orientation of word combinations. However, most of these terms are translated literally (calquing), that is, they have a concrete analogue in the target languages. The multicomponent nature in this case is determined by the complexity of the described physical concepts and phenomena and indicates a poorly developed term system. Descriptive translation is practically absent, which indicates the universality of the terms used and their wide distribution in different languages.

Time-Weighted Kernel-Sparse-Representation-Based Real-Time Nonlinear Multimode Process Monitoring

Real-time nonlinear multimode process monitoring of actual industrial systems has attracted increasing attention recently. In this article, the time-weighed kernel sparse representation (TWKSR) method is proposed to partition the mode of the training dataset by introducing the time-series-dependent characteristics into the kernel sparse representation algorithm. The alternating direction method of multipliers is utilized to solve the optimization problem of the proposed TWKSR method. Then, the representative samples from each identified mode are selected to update the dictionary matrix. Based on the updated dictionary matrix, the sparse coefficient is used for online mode identification, and the reconstruction error is utilized for fault detection. Finally, a numerical simulation case and the wastewater treatment process example verify the effectiveness of the proposed method.

Development of the Japanese Version of the Linguistic Inquiry and Word Count Dictionary 2015.

The Linguistic Inquiry and Word Count Dictionary 2015 (LIWC2015) is a standard text analysis dictionary that quantifies the linguistic and psychometric properties of English words. A Japanese version of the LIWC2015 dictionary (J-LIWC2015) has been expected in the fields of natural language processing and cross-cultural research. This study aims to create the J-LIWC2015 through systematic investigations of the original dictionary and Japanese corpora. The entire LIWC2015 dictionary was initially subjected to human and machine translation into Japanese. After verifying the frequency of use of the words in large corpora, frequent words and phrases that are unique to Japanese were added to the dictionary, followed by recategorization by psychologists. The updated dictionary indicated good internal consistency, semantic equivalence with the original LIWC2015 dictionary, and good construct validity in each category. The evidence suggests that the J-LIWC2015 dictionary is a powerful research tool in computational social science to scrutinize the psychological processes behind Japanese texts and promote standardized cross-cultural investigations in combination with LIWC dictionaries in different languages.

Low-dose PET image denoising based on coupled dictionary learning

Reducing the doses of tracers in positron emission tomography (PET) scans can effectively reduce the damage caused to the patient’s health, but at the same time, doing so reduces the quality of the resulting image. The purpose of this study is to reduce the noise in low-dose (LD) PET images through a coupled dictionary learning method and improve the feature similarity between LD images and standard-dose images. The main method in this paper involves training a coupled dictionary with LD and standard-dose PET image samples and then utilizing the updated dictionary to reconstruct a denoised image. This article changes the dictionary training process by not directly updating the joint dictionary, but rather updating the two dictionaries separately in a specific manner. We compare a variety of traditional denoising algorithms that are applied to LD PET images. The proposed method significantly improves the quality of the resulting image, effectively removing the noise from the image and better retaining the image details. In addition, the reconstructed image is closest to the standard-dose PET image.

A sparse dictionary learning-based denoising method for underwater acoustic sensors

The complex and changeable marine environment will cause the signal detected by underwater acoustic sensors to be extremely weak, and traditional signal processing methods are usually complicated. To improve the detection efficiency and application scope of underwater acoustic sensors, we use sparse decomposition theory and dictionary learning algorithms to directly study the denoising of single hydrophone signals under different sea conditions. Then we use pulse compression technology to solve the problem of traditional methods that long signal causes construction difficulties of the dictionary. The method in this paper can randomly construct a discrete cosine transform (DCT) dictionary without a priori information. The noisy signal is trained and updated via orthogonal matching pursuit (OMP) and method of optimal directions (MOD), and the reconstruction of the signal is completed according to the updated dictionary and sparse coefficients. The results demonstrate that the method in this paper can be applied to wideband long pulse signals, and the signal-to-noise ratio (SNR) gain can reach about 20 dB under different sea conditions. By comparing with the OMP algorithm, the proposed method can reduce the number of atoms so that improving the system performance, reduce the algorithm complexity, and improve the operation efficiency.

State-of-the-art on monolingual lexicography for Basque (Basque)

In this article, we give an overview of the evolution of Basque lexicography to the present, pointing out its main achievements and shortcomings, as well as its challenges for the future. Basque lexicography has a relatively short history, but a considerable amount of resources have been produced in the last 50 years, since the standardisation process began. After years of lexicographic work by different groups and publishers, a remarkable achievement is the Dictionary of the Academy (Euskaltzaindiaren Hiztegia), a prescriptive updated dictionary recently published and based on historical and contemporary corpora. Although the number of monolingual products is noticeably increasing in the last years, Basque dictionary making has been specially productive for bilingual purposes, due probably to the sociolinguistic status of the language. On the other hand, specialized lexicography and terminology have been very active from the beginning of the standadisartion process. Since the beginning of the XXI. century, use of corpora has known an increasing impulse. Many Basque dictionaries are freely available on the Internet.

Fisher Discriminative Coupled Dictionaries Learning

As a recently proposed technique, dictionary learning (DL) has been extensively studied in the field of pattern recognition. Most scholars use a sparse representation as the basic formula for DL while incorporating other techniques into the DL process for obtain an expected dictionary, and exploring a problem with an $$ l_{0} $$-norm or $$ l_{1} $$-norm. However, these strategies increase the time complexity and require additional classifier-aided classification work. In this paper, we propose a novel form of DL called Fisher discriminative coupled dictionaries learning based on general dictionary learning. We use an $$ l_{2} $$-norm to improve the training speed. On embedding the Fisher discrimination into the process of DL, the updated dictionary contains the discriminant information. We update the sample dictionary and coefficient projection dictionary simultaneously as a “dictionary pair”. The sample dictionary is used directly for image classification. The superiority of the proposed method is proven through exhaustive experiments on the AR, extended Yale-B, Scene 15, and Caltech-101 databases.

Bearing fault diagnosis with nonlinear adaptive dictionary learning.

The monitoring of rotating machinery condition has been a critical component of the Industry 4.0 revolution in enhancing machine reliability and facilitating intelligent manufacturing. The introduction of condition-based monitoring has effectively reduced the catastrophic events and maintenance cost across various industries. One of the major challenges of the diagnosis remains as majority of the diagnostic model requires off-line analysis and human intervention. The offline analysis, which is normally done by previous experience, involves tuning model parameters to improve the performance of the diagnostic model. However, for newly developed models, the knowledge of the unknown parameters does not exist. One way to resolve this issue is through learning using adaptation. The adaptation algorithm adjusts itself by newly acquired data. Hence, improvement of the model performance is achieved. In this paper, a nonlinear adaptive dictionary learning algorithm is proposed to achieve early fault detection of bearing elements without using the conventional computation heavy algorithm to update the dictionary. Deterministic and random data separation is implemented using the autoregressive model to reduce the background noise. The filtered data is further analyzed by the Infogram to reveal the impulsiveness and cyclostationary signature of the vibration signal. The dictionary is initialized using random parameters. Instead of using the k means singular value decomposition algorithm to compute the dictionary for adaptation, the unscented Kalman filter (UKF) is implemented to update the dictionaries using the filtered signal from the Infogram. The updating algorithm does not require computation of the dictionary, and no previous knowledge of the dictionary's parameters is needed. The updated dictionary contains the detected fault signature from the Infogram and, therefore, is used for further fault analysis. The proposed algorithm has the advantage of self-adaptation, the capability to map the non-linear relationship of the signal and dictionary weights. The algorithm can be used in the various condition-based monitoring of rotating machineries to avoid additional human efforts and improve the performance of the diagnostic model.

Semi-automating the Reading Programme for a Historical Dictionary Project

This paper describes the resources and software procedures used or developed in a major enabling step towards the revision of the scholarly reference work A Dictionary of South African English on Historical Principles ( DSAE , Silva et al. 1996), namely the semi-automatic generation of a digitally-sourced lexical database on which new and updated dictionary entries will be based; as well as the addition, in parallel, of a new corpus of South African English (SAE) to the project. Drawing on online data sources and an extensive list of known SAE word forms, we have developed a software toolchain to gather, encode, annotate and collate textual sources, producing: (i) a 3.1-billion part-of-speech-annotated corpus of South African English; (ii) a lexical database of illustrative quotations for over 20,000 known SAE word forms, available for selection at the entry-revision stage; and (iii) a list of potential new variant spellings and headword inclusion candidates. These steps replace, where recent electronic sources are concerned, the mechanical aspects of quotation gathering, normally undertaken manually through a reading programme requiring years of teamwork to acquire sufficient coverage (cf. Hicks 2010).

Separation of multiple backscattered echoes using dictionary updating sparse method

Sparse signal representation method has been proven to be effective to estimate and separate the multiple backscattered echoes from underwater target. Usually, the transmitted signal is used as a template to generate the dictionary. It is efficient under assumptions that the knowledge of signal components is perfectly known, or the deviations from the dictionary can be ignored. However, the properties of the echoes differ from the clean reference signal because of the scattering mechanism. In contrast, it has necessity to extract these deviations to have deep insight of the target. Herein, we propose a sparse method that allows the dictionary to be updated. The initial dictionary is defined as a usual way, i.e., based on the transmitted signal. Then, we use a constrained optimization problem to generate dictionary that differs from the original one, introducing an updating procedure. We introduce two constraints, one is to penalize the updated dictionary to be sparse and the other one is to ensure the elements do not deviate too much from the original dictionary. In this way, the resulting dictionary can be used to reconstruct the backscattered echoes, which is illustrated to be efficient with high accuracy by simulated and experimental results.

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

Learning Ancestral Atom via Sparse Coding

Sparse signal models have been the focus of recent research. In sparse coding, signals are represented with a linear combination of a small number of elementary signals called atoms, and the collection of atoms is called a dictionary. Design of the dictionary has strong influence on the signal approximation performance. Recently, to put prior information into dictionary learning, several methods imposing a certain kind of structure on the dictionary are proposed. In this paper, like wavelet analysis, a dictionary for sparse signal representation is assumed to be generated from an ancestral atom, and a method for learning the ancestral atom is proposed. The proposed algorithm updates the ancestral atom by iterating dictionary update in unstructured dictionary space and projection of the updated dictionary onto the structured dictionary space. The algorithm allows a simple differential geometric interpretation. Numerical experiments are performed to show the characteristics and advantages of the proposed algorithm.

Elements of [formula omitted]-ast proton decay

Gauge coupling unification in the Minimal Supersymmetric Standard Model (MSSM) strongly suggests the existence of a Grand Unified Theory (GUT), which could be probed by the observation of proton decay. Proton lifetime in the p → ( e | μ ) + π 0 dimension six mode is proportional in the fourth power to the GUT mass scale, and inversely proportional in the fourth power to the GUT coupling. Flipped SU ( 5 ) with strict MSSM field content is known to survive existing null detection limits for proton decay approaching 10 34 years , and indeed, the lifetime predicted by prior studies can be so long that successful detection is not currently plausible. We provide an updated dictionary of solutions for the relevant flipped unification parameters with generic β-function coefficients, significantly upgrading the level of detail with which second order effects are treated, and correcting subtle published errors. Recently studied classes of F -theory derived GUT models postulate additional vector-like multiplets at the TeV scale which modify the renormalization group to yield a substantial increase in the SU ( 3 ) C × SU ( 2 ) L unified coupling. In conjunction with the naturally depressed F -lipped SU ( 5 ) partial unification mass M 32 , the F -resh analysis undertaken predicts comparatively F -ast proton decay which only narrowly evades existing detection limits, and likely falls within the observable range of proposed next generation detectors such as DUSEL and Hyper-Kamiokande. The TeV-scale vector multiplets are themselves suitable for cross correlation by the Large Hadron Collider. Their presence moreover magnifies the gap between the dual mass scales of Flipped SU ( 5 ) , allowing for an elongated second stage renormalization, pushing the F -inal grand unification to the doorstep of the reduced Planck mass.

Chambers dictionary of science and technology

Covering a diverse range of subjects including astronomy, behaviour, civil engineering, mineral extraction, textiles and zoology, this updated dictionary has over 50,000 entries, and 100 new panel entries to present topics in more detail.

The dictionary of financial risk management

This updated dictionary gives the recent financial vocabulary with regards to derivatives and financial risk. Risk management terminology comes from a number of different markets, including: cash; forwards/futures; swaps; options; and many disciplines such as - economics, tax and financial accounting; probability and statistics; and the law. Each entry is clearly defined and illustrated for clarity.