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We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a key role in a wide range of domains, including, for example, neuroscience, signal processing, and machine learning. First, by leveraging the theory of proximal operators, we relate the equilibria of a family of continuous-time firing-rate neural networks to the optimal solutions of sparse reconstruction problems. Then we prove that the PFCN is a positive system and give rigorous conditions for the convergence to the equilibrium. Specifically, we show that the convergence depends only on a property of the dictionary and is linear-exponential in the sense that initially, the convergence rate is at worst linear and then, after a transient, becomes exponential. We also prove a number of technical results to assess the contractivity properties of the neural dynamics of interest. Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without non-negativity constraints. Finally, we validate the effectiveness of our approach via a numerical example. [ABSTRACT FROM AUTHOR]

We determine the structure of the graded ring of Siegel modular forms of degree 3. It is generated by 19 modular forms, among which we identify a homogeneous system of parameters with 7 forms of weights 4, 12, 12, 14, 18, 20 and 30. We also give a complete dictionary between the Dixmier-Ohno invariants of ternary quartics and the above generators. [ABSTRACT FROM AUTHOR]

Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies. Author summary: We introduce a novel method for dictionary learning termed online convex Network Dictionary Learning (online cvxNDL). The method operates in an online manner and utilizes representative subnetworks of a network dataset as dictionary elements. A key feature of online cvxNDL is its ability to work with graph-structured data and generate dictionary elements that represent convex combinations of real data points, thus ensuring interpretability. Online cvxNDL is used to investigate long-range chromatin interactions in S2 cell lines of Drosophila Melanogaster obtained through RNAPII ChIA-Drop measurements represented as hypergraphs. The results show that dictionary elements can accurately and efficiently reconstruct the original interactions present in the data, even when subjected to convexity constraints. To shed light on the biological relevance of the identified dictionaries, we perform Gene Ontology enrichment and RNA-seq coexpression analyses. These studies uncover multiple long-range interaction patterns that are chromosome-specific. Furthermore, the findings affirm the significance of convex dictionaries in representing TADs cross-validated by imaging methods (such as 3-color FISH (fluorescence in situ hybridization)). [ABSTRACT FROM AUTHOR]

Salient object detection is an increasingly popular topic in the computer vision field, particularly for images with complex backgrounds and diverse object parts. Background information is an essential factor in detecting salient objects. This paper suggests a robust and effective methodology for salient object detection. This method involves two main stages. The first stage is to produce a saliency detection map based on the dense and sparse reconstruction of image regions using a refined background dictionary. The refined background dictionary uses a boundary conductivity measurement to exclude salient object regions near the image's boundary from a background dictionary. In the second stage, the CascadePSP network is integrated to refine and correct the local boundaries of the saliency mask to highlight saliency objects more uniformly. Using six evaluation indexes, experimental outcomes conducted on three datasets show that the proposed approach performs effectively compared to the state-of-the-art methods in salient object detection, particularly in identifying the challenging salient objects located near the image's boundary. These results demonstrate the potential of the proposed framework for various computer vision applications. [ABSTRACT FROM AUTHOR]

Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size. [ABSTRACT FROM AUTHOR]

The 19th century saw the important transformation of modern Western concepts into Chinese lexical resources. The missionaries were the initiators and important driving force for the translation of Western books into Chinese in modern China. They promoted 'translating terms' and 'coining terms' in their translations of Western books and the compilation of dictionaries with the cooperation of Chinese intellectuals. Their work provided a tangible 'word' carrier of 'concepts' for disseminating modern knowledge from the West to the East. Compiled by missionaries, the English–Chinese bilingual dictionaries introduced a brand-new concept of dictionary compilation and changed China's history of having zidian (字典, character dictionaries) but no cidian (辞典, specialized dictionaries). In particular, John Fryer applied the translation method of creating new words or characters in the translation of chemical terminology. Members of the School and Textbook Series Committee, including John Fryer and Calvin Wilson Mateer, made great contributions to theories and strategies for translation, which keep inspiring Chinese–English translation of terminology and its theoretical construction. [ABSTRACT FROM AUTHOR]

A strong case can be made that the concept of naskh, "abrogation" or "annulment", was the most potent weapon in the arsenal of Muslim polemicists seeking to convert Jews (Burton's Encyclopaedia of the Qurʾān is highly informative but deals almost exclusively with naskh in its internal Islamic contexts, e.g., hermeneutics and legal theory). Naskh did not necessarily involve any rejection of Jewish scripture or tradition as fraudulent or corrupt. It rested on the simple premise, explicitly confirmed by the Qur'an, that the deity may alter or replace His legislation over the course of time. In the first part of this paper, I will briefly review the topic, adding some texts and observations that, to the best of my knowledge, have not appeared in the academic literature (comprehensively surveyed in Adang's Muslim Writers on Judaism and the Hebrew Bible: From Ibn Rabban to Ibn Hazm, 1996; also in Adang and Schmidtke's Polemics (Muslim-Jewish) in Encyclopedia of Jews in the Islamic World, 2010). The bulk of this paper will consist of a fairly detailed summary of an unpublished tract on naskh written by Rashīd al-Dīn Faḍlullāh Hamadānī (RD) (1247–1318), himself a Jewish convert to Islam and a monumental politician, cultural broker, historian, and author. [ABSTRACT FROM AUTHOR]

The fourth edition of the Oxford Dictionary of the Christian Church, edited by Andrew Louth, is a significant event in the field. The first two editions were influenced by Anglicanism, but the third edition reflected changing theological climates. The current edition expands the number of articles on Christianity and includes traditional churches, new movements, leaders, and theological concerns. The edition also addresses contemporary issues and includes over 480 contributors and 6500 entries. While there may be some subjective decisions on what to include, overall, the edition maintains a sense of balance and proportion. [Extracted from the article]

This paper proposes an improved matching pursuit (MP) method with a multicomponent interference dictionary (MCID) based on a pipeline guided wave reflection model. The proposed method separates the overlapped time domain reflections from the notch edges and extracts parameters such as amplitude, time of flight (TOF), and phase to identify the number and axial dimensions of notches in pipes. Firstly, the performance of the method in identifying parameters of overlapped components affected by noise and reverberation under different signal-to-noise ratios (SNRs) and frequencies is analyzed using four error metrics. Secondly, finite-element (FE) models of pipes with a single notch and double notches are established, and the accuracy of the reflection model is validated by comparing the predicted amplitudes, TOF, and phase of reflections with the FE results. Finally, experimental validation is conducted on aluminum pipes with multiple notches. The consistency between the experimental results, theoretical results, and FE results in wave packet parameters confirmed the accuracy of the proposed reflection models. The method accurately captures the parameters of the reflections from each notch edge in the experimental pipes, enabling the identification of the number and axial dimensions of the notches. The method holds the potential for identifying a greater number of notches in pipelines and characterizing their axial dimensions. This can provide a reliable reference for developing maintenance plans based on pipeline operational conditions, thereby preventing major safety incidents. [ABSTRACT FROM AUTHOR]

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/. [ABSTRACT FROM AUTHOR]

Computing k-mer frequencies in a collection of reads is a common procedure in many genomic applications. Several state-of-the-art k-mer counters rely on hash tables to carry out this task but they are often optimised for small k as a hash table keeping keys explicitly (i.e., k-mer sequences) takes O (N k w) computer words, N being the number of distinct k-mers and w the computer word size, which is impractical for long values of k. This space usage is an important limitation as analysis of long and accurate HiFi sequencing reads can require larger values of k. We propose Kaarme, a space-efficient hash table for k-mers using O (N + u k w) words of space, where u is the number of reads. Our framework exploits the fact that consecutive k-mers overlap by k - 1 symbols. Thus, we only store the last symbol of a k-mer and a pointer within the hash table to a previous one, which we can use to recover the remaining k - 1 symbols. We adapt Kaarme to compute canonical k-mers as well. This variant also uses pointers within the hash table to save space but requires more work to decode the k-mers. Specifically, it takes O (σ k) time in the worst case, σ being the DNA alphabet, but our experiments show this is hardly ever the case. The canonical variant does not improve our theoretical results but greatly reduces space usage in practice while keeping a competitive performance to get the k-mers and their frequencies. We compare canonical Kaarme to a regular hash table storing canonical k-mers explicitly as keys and show that our method uses up to five times less space while being less than 1.5 times slower. We also show that canonical Kaarme uses significantly less memory than state-of-the-art k-mer counters when they do not resort to disk to keep intermediate results. [ABSTRACT FROM AUTHOR]

The double copy is a remarkable relationship between gauge theory and gravity that has been explored in a number of contexts, most notably scattering amplitudes and classical solutions. The convolutional double copy provides a straightforward method to bridge the two theories via a precise map for the fields and symmetries at the linearised level. This method has been thoroughly investigated in flat space, offering a comprehensive dictionary both with and without fixing the gauge degrees of freedom. In this paper, we extend this to curved space with an (anti) de Sitter background metric. We work in the temporal gauge, and employ a modified convolution that involves the Mellin transformation in the time direction. As an example, we show that the point-like charge in gauge theory double copies to the (dS-) Schwarzschild black hole solution. [ABSTRACT FROM AUTHOR]

According to the AdS/CFT dictionary, adding a relevant double-trace deformation f ∫ O2 to a holographic CFT action is dual to imposing mixed Neumann/Dirichlet boundary conditions for the field dual to O in AdS. We observed similar behaviour in codimension-two flat space holographies. We consider deformations of boundary conditions in flat spacetimes under flat space codimension-two holographies, Celestial holography and Wedge-like holography. In the former Celestial-holographic approach, we imposed boundary conditions on initial and final bulk states in the scattering. We find that these non-trivial boundary conditions in the bulk induce "double deformations" on the Celestial CFT side, which can be understood as an analogy of double trace deformations in the usual AdS/CFT. We compute two-point bulk scattering amplitudes under the non-trivial deformed boundary conditions. In the latter Wedge-like holography approach, we consider mixed Neumann/Dirichlet boundary conditions on the null infinity of the light-cone. We find that this mixing induces a renormalization flow in the dual Wedge CFT side under the Wedge holography, as in the usual AdS/CFT. We argue that the discrepancy between the Wedge two-point function and the Celestial two-point function originates from a sensitivity of bulk massless fields to a regularization parameter to use the usual AdS/CFT techniques. [ABSTRACT FROM AUTHOR]

Data-driven approximations of the Koopman operator are promising for predicting the time evolution of systems characterized by complex dynamics. Among these methods, the approach known as extended dynamic mode decomposition with dictionary learning (EDMD-DL) has garnered significant attention. Here, we present a modification of EDMD-DL that concurrently determines both the dictionary of observables and the corresponding approximation of the Koopman operator. This innovation leverages automatic differentiation to facilitate gradient descent computations through the pseudoinverse. We also address the performance of several alternative methodologies. We assess a "pure" Koopman approach, which involves the direct time-integration of a linear, high-dimensional system governing the dynamics within the space of observables. Additionally, we explore a modified approach where the system alternates between spaces of states and observables at each time step—this approach no longer satisfies the linearity of the true Koopman operator representation. For further comparisons, we also apply a state-space approach (neural ordinary differential equations). We consider systems encompassing two- and three-dimensional ordinary differential equation systems featuring steady, oscillatory, and chaotic attractors, as well as partial differential equations exhibiting increasingly complex and intricate behaviors. Our framework significantly outperforms EDMD-DL. Furthermore, the state-space approach offers superior performance compared to the "pure" Koopman approach where the entire time evolution occurs in the space of observables. When the temporal evolution of the Koopman approach alternates between states and observables at each time step, however, its predictions become comparable to those of the state-space approach. [ABSTRACT FROM AUTHOR]

Scalable numerical solutions to the time dependent Schrodinger equation remain an outstanding goal in theoretical chemistry. Here we present a method which utilises recent breakthroughs in signal processing to consistently adapt a dictionary of basis functions to the dynamics of the system. We show that for two low-dimensional model problems the size of the basis set does not grow quickly with time and appears only weakly dependent on dimensionality. The generality of this finding remains to be seen. The method primarily uses energies and gradients of the potential, opening the possibility for its use in on-the-fly ab initio quantum wavepacket dynamics. [ABSTRACT FROM AUTHOR]

In the studies of classical Chinese poetry, the comparison between Li Bai and Du Fu is an everlasting topic, yielding many qualitative interpretations, among which a widely known but disputable one is Li's positivity versus Du's negativity. With the development of digital means, distant reading has become possible, and the sentiment issue can be further explored in quantitative ways. This research conducts a corpus-based sentiment comparison of Li and Du with a self-constructed sentiment dictionary. The Complete Collection of Tang Poems is used as a representative of Tang poets, and sentiment comparisons are made at the levels of poems, verses, and characters, as well as key characters extracted with the log-likelihood measure. Analyses show that (1) among Tang poets, Du is more negative at all of the above textual levels, while Li is only more positive at the key character level, proving the importance of key characters in readers' perception of sentiment; (2) Li and Du both stand out among Tang poets with a negative depiction of the dark reality and a positive expression of grand ideals; and (3) Li's positivity is largely embodied in his depictions of color, light, and temperature, while Du's negativity is closely related to his psychological description. To conclude, this research has not only determined the sentiment difference between Li and Du but also located its sources in texts with a novel key character-based sentiment analysis approach. Keywords: [ABSTRACT FROM AUTHOR]

In this article, we study approximation properties of the variation spaces corresponding to shallow neural networks with a variety of activation functions. We introduce two main tools for estimating the metric entropy, approximation rates, and n-widths of these spaces. First, we introduce the notion of a smoothly parameterized dictionary and give upper bounds on the nonlinear approximation rates, metric entropy, and n-widths of their absolute convex hull. The upper bounds depend upon the order of smoothness of the parameterization. This result is applied to dictionaries of ridge functions corresponding to shallow neural networks, and they improve upon existing results in many cases. Next, we provide a method for lower bounding the metric entropy and n-widths of variation spaces which contain certain classes of ridge functions. This result gives sharp lower bounds on the L 2 -approximation rates, metric entropy, and n-widths for variation spaces corresponding to neural networks with a range of important activation functions, including ReLU k activation functions and sigmoidal activation functions with bounded variation. [ABSTRACT FROM AUTHOR]

In this article, relying on Readex's electronic database 'Early American Newspapers, Series 1, 1690–1876: From Colonies to Nation', I use the evidence of newspapers to construct a picture of the encyclopaedias most mentioned in British North America and the early United States during the period before 1790. I begin by explaining my methodology and reviewing other approaches to studying the American market for encyclopaedias before 1790. Then, working on the assumption that mentions of encyclopaedias in newspapers roughly represent interest in encyclopaedias, I attempt to quantify Americans' evolving interest in different encyclopaedias and kinds of encyclopaedias. Lastly, having sketched out the landscape of encyclopaedias in eighteenth-century America, I consider the uses to which they were put. Among other conclusions, I argue that, despite assertions to the contrary by encyclopaedists and their publishers, encyclopaedias functioned less as substitutes for private libraries than as complements to them. [ABSTRACT FROM AUTHOR]

In this paper, Lamb mode identification method based on small-sample dictionary algorithm is proposed and applied for the separation of specific Lamb modes, the reconstruction of Lamb waves upon propagating a certain distance and damage identification. This approach includes the creation of small-sample dictionary and querying procession in a dictionary. Firstly, Lamb wave signals upon propagating at a series of distances are simulated, and signal features, {mode, distance, time of flight (Tof), wavelet energy}, are extracted to create a dictionary; secondly, Tof of the received signal is extracted, and then Lamb modes are identified by searching the dictionary; finally, energy parameters are estimated to reconstruct wavepackets. The feasibility of this algorithm is validated in AAA laminate, and the results are presented. In a 2D-simulation model of a pitch-catch configuration, A0 and S0 modes can be identified and reconstructed effectively when the direct waves and the reflected waves are synchronously received, with the propagation distance of 0.3 m and 0.5 m, respectively. In addition, a Lamb-wave-based delamination location is conducted in three-dimensional AAA laminate. The experimental results show that the delamination can be located relatively by combining the identified damage-scattered S0 waves and the probability-based diagnostic imaging. [ABSTRACT FROM AUTHOR]

We develop a method to assess the three indicators of transactive memory systems (TMS)—specialization, credibility, and coordination—through computer-aided text analysis. First, human coders assessed group transcripts for phrases representative of these indicators. From those phrases, we identified words that occurred frequently to develop a dictionary of TMS indicators. In total, we analyzed 262 groups composed of 1,091 individuals. Both the human-coded and dictionary-based assessments of TMS indicators are significantly related to a popular survey-based assessment of TMS. Our approach could be used to advance understanding of TMS by analyzing it in contexts where administering surveys is not feasible. [ABSTRACT FROM AUTHOR]

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model. [ABSTRACT FROM AUTHOR]

Mufti Ahmad Yar Khan Naemī was a Famous scholar of 14th century of Hijra in the history of subcontnent. He was a great mofasir of Quran. He wrote Tafseer Naemī which he could not complete and he died during this great job. He wrote the tafseer of only 11 paras only. His son started to write it but he could complete only 20 paras and died. In this article Drayati Usloob of Mufti Ahmad Yar Khan has been discussed and analysed in the description of Tasawwuf. Mufti Ahmad Yar Khan has explained the verses in different categories. He narrates the meanings of a verse by Arabic dictionary, words and their meanings, translation of Ahmad Raza Khan brelwi, relation of the verse with earlier one, Shan e Nazool, Nahwi Tafseer, scholarly Tafseer, Fawaid e Ayat, Fiqhi Masail, Answers to different objections and Sufiana Tafseer. His Sufiana Tafseer has points to about tasawwuf and its terms. It can be concluded that he has also used Drāyat in elaborating the matters o Tasawwuf in his tafseer. [ABSTRACT FROM AUTHOR]

This article outlines essential best practices for retro-digitized dictionary projects, using the ongoing MORDigital project (DOI 10.54499/PTDC/LLT-LIN/6841/2020) as a case study. The MORDigital project focuses on digitally transforming the historically significant Portuguese Morais dictionary's first three editions (1789, 1813, 1823). While the primary objective is to create faithful digital versions of these renowned dictionaries, MORDigital stands out by going beyond the mere adoption of established best practices. Instead, it reflects on the choices made throughout the process, providing insights into the decision-making process. The key topics emphasized include (1) the establishment of a robust data model; (2) the refinement of metadata; (3) the implementation of consistent identifiers; and (4) the enhancement of encoding techniques; additionally exploring the issue of structuring domain labelling. The article aims to contribute to the ongoing discourse on best practices in retro-digitized dictionary projects and their implications for data preservation and knowledge organization. [ABSTRACT FROM AUTHOR]

Currently, word segmentation errors and polysemy problems are common in the field of Chinese relationship extraction. Although character-based model input can avoid word segmentation errors, in order to obtain the word information of a sentence, it is often necessary to introduce a dictionary or an external knowledge base to expand the word information, which requires a lot of manpower and time. In response to the above existing problems, this article uses characters as input, uses multiple embedding models to jointly form a character vector sequence, and obtains features containing character information through BiLSTM and attention layers; considering that convolutional neural networks are good at extracting local features, obtain features containing word information through multi-kernel convolutional layers and multi-head self-attention layers, and finally use a gating mechanism to fuse the features. The model was tested on the public SanWen data set and our own cultural-travel data set, and obtained F1 values of 61.22% and 60.26% respectively. Experimental results show that our method can achieve better relationship extraction effects without using word segmentation tools and without building a dictionary or external knowledge base, and the effect is better than most commonly used models currently. [ABSTRACT FROM AUTHOR]

In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1500 generic medicines. It presents an innovative method of storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, and queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as a diverse range of healthcare professionals, and AI applications. [ABSTRACT FROM AUTHOR]

Moral features are essential components of TV series, helping the audience to engage with the story, exploring themes beyond sheer entertainment, reflecting current social issues, and leaving a long-lasting impact on the viewers. Their presence shows through the language employed in the plot description. Their detection helps regarding understanding the series writers' underlying message. In this paper, we propose an approach to detect moral features in TV series. We rely on the Moral Foundations Theory (MFT) framework to classify moral features and use the associated MFT dictionary to identify the words expressing those features. Our approach combines that dictionary with word embedding and similarity analysis through a deep learning SBERT (Sentence-Bidirectional Encoder Representations from Transformers) architecture to quantify the comparative prominence of moral features. We validate the approach by applying it to the definition of the MFT moral feature labels as appearing in general authoritative dictionaries. We apply our technique to the summaries of a selection of TV series representative of several genres and relate the results to the actual content of each series, showing the consistency of results. [ABSTRACT FROM AUTHOR]

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion–based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis -regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data. [ABSTRACT FROM AUTHOR]

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P -values. [ABSTRACT FROM AUTHOR]

The article presents comparative analyses of the 1989 and 2022 revised editions of the "Oxford English Dictionary," focusing on the antedating of entries. It discusses the incidence of revised earliest dates of bibliographical adjustments, number of entries revised with near antedates, longer antedatings, antedatings attributed to the availability of high-quality historical text databases, and revisions to the 1989 edition earliest citations by quarterly update and original earliest date.

Text psycholinguistic features are a valuable source for various research topics since they are used to obtain psychological, social, and linguistic aspects from written texts using dictionary files. These files are structured in categories, which are defined as groups of dictionary words that tap a particular domain (e.g., negative emotion words). The Linguistic Inquiry Word Count (LIWC) is a vastly used and versatile computer-based language analysis tool designed for text psycholinguistic analysis. The most recent version of the default English dictionary is LIWC2015, as it was released with the 2015 version of the LIWC software. The literature has recently introduced the latest Brazilian Portuguese LIWC dictionary (BP-LIWC2015), developed with the same categories as the LIWC 2015 English dictionary. However, the literature has also reported the need to evaluate BP-LIWC2015. In this scenario, this work investigates three questions: (i) Since LIWC2015 shows consistent improvements over the English dictionary developed in 2007 (LIWC2007), does BP-LIWC2015 achieves better text classification results than the older Brazilian Portuguese dictionary (BP-LIWC2007)? (ii) What is the equivalence between BP-LIWC2015 and BP-LIWC2007 with LIWC2015? (iii) Are there significant differences between Brazilian Portuguese dictionaries? To answer these questions, we conducted text classification experiments with four datasets and seven classification algorithms to compare the two Brazilian Portuguese LIWC dictionaries reported in the literature (i.e., 2007 and 2015). Second, we used a bilingual Portuguese-English scientific news collection to analyze the correlation between LIWC2015 and Brazilian Portuguese LIWC dictionaries. The results indicate that BP-LIWC2015 outperforms the older version in Brazilian Portuguese text classification. Finally, we found a more significant correlation between BP-LIWC2015 and the original English dictionary than the older version. [ABSTRACT FROM AUTHOR]

In this article, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framework based on marginal model likelihood, and we develop an efficient computational algorithm for inference. We establish a novel high-dimensional theory for this nonlinear multivariate regression. Our theory is general, allowing for potential correlations between the binary responses. We propose a new type of nuclear norm penalty using the smooth clipped absolute deviation, filling the gap in the related non-convex penalization literature. We theoretically demonstrate that the proposed approach improves estimation accuracy by considering multiple responses jointly through the proposed estimator when the underlying coefficient matrix is low-rank and row-wise sparse. In particular, we establish the non-asymptotic error bounds, and both rank and row support consistency of the proposed method. Moreover, we develop a consistent rule to simultaneously select the rank and row dimension of the coefficient matrix. Furthermore, we extend the proposed methods and theory to a joint Ising model, which accounts for the dependence relationships. In our analysis of both simulated data and the cancer cell line encyclopedia data, the proposed methods outperform the existing methods in better predicting responses. for this article are available online. [ABSTRACT FROM AUTHOR]

Introduction: In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study. Objective: We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification. Methods: We retrieved all drug names reported to the FAERS (2004–December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification. Results: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%). Conclusion: The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the ways in which mock jurors justified their verdict decisions using moral foundations language. Participants read a trial transcript describing a second-degree murder charge featuring an automatism plea (which negates the physical volition of a crime). They then provided a two-to-three sentence rationale for their verdict choice, which we coded for the contextually-valid presence of words from the Moral Foundations (MF) Dictionary. Mock jurors were most likely to use harm-related language in justifying murder votes. A qualitative description also revealed differences in the content of the justifications. [ABSTRACT FROM AUTHOR]

Recently, video summarization (VS) has emerged as one of the most effective tools for rapidly understanding video big data. Dictionary selection based on self‐representation and sparse regularization is consistent with the requirement of VS, which aims to represent the original video with little reconstruction error by a small number of video frames. However, one crucial issue is that the existing methods mainly use a single view feature, which is not sufficient enough for acquiring the full pictorial details and affects the quality of the produced video summary. Although a few methods use more than one features, they only directly concatenate the features, which does not take advantage of the relationship of different features. Considering the complementarity of shallow and deep features, multiview feature co‐factorization based dictionary selection for VS is proposed in this paper to use the common information of both view features for VS. Specifically, two view features are used to fully exploit the full pictorial information of video frames, then the common information of two different views is extracted through coupled matrix factorization to conduct the dictionary selection for VS. Experiments have been carried out on two benchmark datasets, and results have demonstrated the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Conditions for a dictionary in a Hilbert space are obtained that are necessary or sufficient for the coincidence of purely greedy and orthogonal greedy algorithms with respect to this dictionary. [ABSTRACT FROM AUTHOR]

A naive celestial dictionary causes massless two-point functions to take the delta-function forms in the celestial conformal field theory (CCFT). We rectify the dictionary, involving the shadow transformation so that the two-point functions follow the standard power-law. In this new definition, we can smoothly take the massless limit of the massive dictionary. We also compute a three-point function using the new dictionary and discuss the OPE in CCFT. [ABSTRACT FROM AUTHOR]

We study various perturbations and their holographic interpretation for non-Abelian T-dual of AdS5 × S5 where the T-duality is applied along the SU(2) of AdS5. This paper focuses on two types of perturbations, namely the scalar and the vector fields on NATD of AdS5 × S5. For scalar perturbations, the corresponding solutions could be categorised into two classes. For one of these classes of solutions, we build up the associated holographic dictionary where the asymptotic radial mode sources scalar operators for the (0 + 1)d matrix model. These scalar operators correspond to either a marginal or an irrelevant deformation of the dual matrix model at strong coupling. We calculate the two point correlation between these scalar operators and explore their high as well as low frequency behaviour. We also discuss the completion of these geometries by setting an upper cut-off along the holographic axis and discuss the corresponding corrections to the scalar correlators in the dual matrix model. Finally, we extend our results for vector perturbations where we obtain asymptotic solutions for a particular class of modes. These are further used to calculate the boundary charge density at finite chemical potential. [ABSTRACT FROM AUTHOR]

The article explores the use of Wikipedia in teaching history in the U.S. Topics include why Wikipedia is the world's most visited website, the lack of knowledge of digital natives about the process by which Wikipedia's articles are written and revised, a discussion of an assignment created at a University of Montevallo history class comparing traditional encyclopedia entries to their Wikipedia equivalents, and an overview of a Digital History elective course created at the university in 2014.

Given the controversies over Mandarin Chinese in terms of Talmy's bipartite language typology, this paper presents an exhaustive study of Chinese motion verbs collected from two authoritative dictionaries, namely, The Ancient Chinese Dictionary (2nd Edition) and The Contemporary Chinese Dictionary (7th Edition). An analysis of 662 motion verbs in ancient Chinese and 693 motion verbs in modern Chinese indicates that Mandarin Chinese has undergone a typological shift from verb-framed to satellite-framed as far as the lexicalization pattern is concerned. The typological shift seems to have been driven by two forces, the decline of monosyllabic motion verbs and the upsurge of disyllabic motion verbs, which, upon second thoughts, can be boiled down to a single but predominant process of disyllabification in Chinese, whereby two (former) roots that bear a wide range of syntactic relations are lexicalized into a disyllabic word. Thus, we see an intriguing case of how phonology and morphosyntax interact to impact the typological properties of a language. [ABSTRACT FROM AUTHOR]

On 26 May 2011, the Russian People's World Council issued a document entitled The Basic Values: The Fundaments of National Unity. The document, prepared by the Synodal Department for Church–Society Cooperation, provided a catalogue of 17 traditional values whose general framework was constituted by a combination of freedom, unity, patriotism, family, and devotion. At that time, the Moscow Patriarchate considered religious faith to be the foundation of traditional values and it continues to do so. The defence and promotion of traditional Russian spiritual and moral values were also central in the Russian National Security Strategy (2015); this was the case in the updated version of this document as well, put out in July 2021. Furthermore, they have been the core of the Moscow Patriarchate's participation in the Council of Europe and of Patriarch Kirill's speeches about the war in Ukraine. Finally, on 9 November 2022, The Foundations Of State Policy For The Preservation Of Spiritual And Moral Values was approved. This framework permits us to understand the strict interplay between the Church and the State in the Russian Federation and to see why it is important to refer to the concept of post-secularism when talking about the role of religion in post-Soviet Russia. Proceeding from the Abstract, the present paper aims to analyse this interplay in a specific sector of visual culture: the cinema and television industries. Manuel Castells highlighted the relevance of cultural values in the age of information and the connection between the values and social mobilization that follows it. He pointed out that the Internet has become a way to render this connection predominant, inevitably leading to the development of social movements and networks that have a religious basis. This is unquestionably true; surveys conducted by the Russian Public Opinion Research Center (OJSC «VCIOM») and by Nevafilm Research confirm that a high percentage of Russians watch films not only at the cinema or on television (especially the older generations) but also on the Internet (as far as the younger generations are concerned). The importance of this market is also confirmed by the success of the cinema and TV distributor Orthodox Encyclopaedia (2005); in the words of the philosopher Sergei Kravets, who, commenting on it during an interview published in 2006 by the website Sedmits.ru, declared that the expression "orthodox cinema" can be understood as a way to express Russian culture. He asserts that "the fact that today Orthodox films have begun to appear on the central TV channels testifies that Russian film producers and viewers have apparently begun to be aware of themselves as Orthodox, to feel that they are bearers of a special Orthodox culture. [..]". At the same time, consideration should be given to the importance of the Russian Orthodox Church and the Minister of Culture's condemnation of films such as Matilda or Monastery. In addition, it is important to consider that, according to a survey conducted in 2022 by the Levada Center, Russian people consider television the most reliable source of information (54%). The long-term implications of this tendency may have very important effects, not only in terms of its objectives but also in terms of the consideration that, after the beginning of the war, many Western film distributors withdrew their licenses from Russia. This paper will analyse "the effect of religion on the institutional system, the regulatory environment of the media and the public sphere" by studying the features of films and TV programs distributed by Orthodox Encyclopaedia, their relations with traditional values promoted both by the Kremlin and the Church, how these have contributed to strengthening the interplay between the Minister of Culture and the Moscow Patriarchate, and the impact this process has had on Russian society and Russia's relations with the European and Western World in the 2005–2022 period. A list of the films and TV programs being discussed will be provided, and then statements about the project and reviews of the serials and films will be analysed. The analysis will be conducted mainly through the official sites of the Russian Orthodox Church and the Kremlin and by browsing the Integrum database. [ABSTRACT FROM AUTHOR]

Most of us are not experts in specific fields, such as ornithology. Nonetheless, we do have general image and language understanding capabilities that we use to match what we see to expert resources. This allows us to expand our knowledge and perform novel tasks without ad-hoc external supervision. On the contrary, machines have a much harder time consulting expert-curated knowledge bases unless trained specifically with that knowledge in mind. Thus, in this paper we consider a new problem: fine-grained image recognition without expert annotations, which we address by leveraging the vast knowledge available in web encyclopedias. First, we learn a model to describe the visual appearance of objects using non-expert image descriptions. We then train a fine-grained textual similarity model that matches image descriptions with documents on a sentence-level basis. We evaluate the method on two datasets (CUB-200 and Oxford-102 Flowers) and compare with several strong baselines and the state of the art in cross-modal retrieval. Code is available at: https://github.com/subhc/clever. [ABSTRACT FROM AUTHOR]

The Cultural Dictionary for German as a Foreign Language is a lexicographical project based at the Technical University of Berlin that aims to compile the cultural vocabulary for advanced learners of German (cf. Roelcke 2015; 2020; Pichler 2021). Its main goal is to enable learners to participate in thematically related discourse. The project is currently conducted at the department of German as a Foreign Language and is mainly carried out by students. The text at hand presents current lexicographical and pedagogical considerations and finally provides an outlook on the future implementation of the project. [ABSTRACT FROM AUTHOR]

The Cultural Dictionary for German as a Foreign Language is a lexicographical project based at the Technical University of Berlin that aims to compile the cultural vocabulary for advanced learners of German (cf. Roelcke 2015; 2020; Pichler 2021). Its main goal is to enable learners to participate in thematically related discourse. The project is currently conducted at the department of German as a Foreign Language and is mainly carried out by students. The text at hand presents current lexicographical and pedagogical considerations and finally provides an outlook on the future implementation of the project. [ABSTRACT FROM AUTHOR]

In various applications, the adoption of optimal energy matching pursuit with dictionary elements is common. When the dictionary elements are indexed by parameters within a bounded region, exhaustion-type algorithms can be employed. This article aims to investigate a process that converts the optimal parameter selection in unbounded regions to a bounded and closed (compact) sub-domain. Such a process provides accessibility for energy matching pursuit in a wide range of applications. The paper initially focuses on the open unit disc and the upper-half complex plane, introducing adaptive Fourier decomposition as the underlying methodology. It then extends this concept to general Hilbert spaces with a dictionary and Bochner spaces for random signals. Computational examples are included to illustrate the concepts presented. [ABSTRACT FROM AUTHOR]

Dynamic Magnetic Resonance Imaging (dMRI) is a valuable tool for understanding changes in human physiology, but its temporal and spatial resolution can be limited. Compressed sensing (CS) has been used to enhance temporal resolution by acquiring partial k-spaces of each time frame and exploiting sparsity to retain spatial resolution. Invoking CS in dMRI necessitates algorithms that can leverage both spatial sparsity within each time frame and temporal sparsity across time frames. A tensor decomposition-based multi-mode dictionary learning algorithm has been proposed to learn the spatial and temporal features of dMRI data and reconstruct it more efficiently. The extensive quantitative simulations reveal the improvement induced by the proposed method in various settings compared to state-of-the-art methods in dMRI. Further, it considerably advances reconstruction speed from trained dictionaries over the state-of-the-art, permitting faster scans catering to a larger patient group. [ABSTRACT FROM AUTHOR]

This study aims to establish what lexical factors make it more likely for dictionary users to consult specific articles in a dictionary using the English Wiktionary log files, which include records of user visits over the course of 6 years. Recent findings suggest that lexical frequency is a significant factor predicting look-up behavior, with the more frequent words being more likely to be consulted. Three further lexical factors are brought into focus: (1) age of acquisition; (2) lexical prevalence; and (3) degree of polysemy operationalized as the number of dictionary senses. Age of acquisition and lexical prevalence data were obtained from recent published studies and linked to the list of visited Wiktionary lemmas, whereas polysemy status was derived from Wiktionary entries themselves. Regression modeling confirms the significance of corpus frequency in explaining user interest in looking up words in the dictionary. However, the remaining three factors also make a contribution whose nature is discussed and interpreted. Knowing what makes dictionary users look up words is both theoretically interesting and practically useful to lexicographers, telling them which lexical items should be prioritized in lexicographic work. [ABSTRACT FROM AUTHOR]

During the early 19th century, a peculiarly systematic way of organizing books emerged in Germany. This systematization, which purported to be a rational organization of subject matter, was an outgrowth of the philosophy of Hegel. This article attempts to outline Hegel's organizational practice. It argues that Hegel's encyclopedia was a reaction against the Enlightenment encyclopedia, and that it attempted to restore the systematic mindset of pre-modern reference books. Yet it did this, not in a straightforward fashion, but by developing a method for organizing material that both stresses the interconnectedness of knowledge and promises to be highly scalable. In its attempt to trace the Hegelian organizational method, this article revisits and calls attention to a forgotten work in Hegel scholarship by Michael John Petry, who was the first writer to seriously stress the importance of classification in Hegel's work. This article is an attempt to demonstrate that Hegelianism and information organization can be brought into fruitful and mutually beneficial dialogue. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the usefulness of examples that show typical learner errors in online pedagogical dictionaries of English for the accuracy of error correction as well as immediate and delayed retention of usage. The optimal positioning of examples of errors in entries is also researched. In an online experiment, participants did a sentence correction exercise with the help of purpose-built monolingual dictionary entries, where the provision and positioning of examples showing errors were controlled. Two test versions were created, which differed only in the presence of examples of errors in the entries. Usage retention was observed immediately after the test and two weeks later. The results indicate that it is worthwhile to include examples of errors in online learners' dictionaries because they contribute greatly to the retention of usage in the long run. They also help to rectify errors, though the effect is not statistically significant. The positioning of examples showing errors in entries has no influence on error correction accuracy or usage retention. The study reveals examples of errors to be a valuable induction-oriented stand-alone dictionary component placed outside warning boxes, which typically include explicit grammar rules and promote deduction. [ABSTRACT FROM AUTHOR]