Similar Vectors Research Articles

A Study of Self Similar Vector Fields in Bianchi Type III Spacetime Via Riftree Approach

Abstract In this research, we study self-similar vector fields in Bianchi type III spacetime by applying
 the Rif tree method. This study uses a computer approach to convert symmetry equations
 into a simplified involutive form, which separates the integration problem into several cases
 that are shown as a tree structure. Certain restrictions apply to the metric functions in
 certain circumstances. Explicit formulations for the metrics and their corresponding self
similar vector fields are obtained by solving the system of equations governing self-similar
 symmetries using these conditions. Additionally, physical significance are taken into ac
count for an in-depth analysis of the generated models.

Using a natural capital risk register to support the funding of seagrass habitat enhancement in Plymouth Sound

Seagrass is an important marine habitat that provides benefits to society in the form of ecosystem services. Services include the provision of food via fisheries, the regulation of water quality and the ability to sequester and store carbon. In the UK, seagrass beds are in decline, increasing the risk of ecosystem service loss. Current efforts to protect, restore and create seagrass habitat, beyond spatial management measures, rely on grant funding and donations. Emerging carbon, biodiversity and wider ecosystem service markets offering potential revenue sources could facilitate the enhancement of seagrass habitat at scale. Participation in ecosystem service markets, requires that projects deliver on the ecosystem service benefits defined. As the benefits will have been paid for, there are risks associated with not delivering on ecosystem service benefits. It is important that the risk is clearly defined. In this study we further the marine natural capital and risk register approach and apply the method to a case study area to support the development of sustainable funding options for seagrass habitat enhancement in Plymouth Sound, UK. Habitat Suitability modelling is also used to map potential areas for seagrass habitat enhancement. We find that, in the Plymouth Sound area, the risk of loss of ecosystem services for subtidal seagrass habitats is, at present, high. This is primarily linked to the declining extent and condition of subtidal seagrass assets. Under current governance, all of Plymouth Sound’s subtidal seagrass are within a Marine Protected Area, though this conservation designation does not guarantee that the seagrass bed is protected from damaging activity. Under current environmental conditions there is opportunity for widespread seagrass restoration and creation. Risk to seagrass beds and any future private funding could be reduced by governance actions that enable effective direct protection of the seagrass assets and mitigate harmful pressures (e.g., reduction of water pollution). Emerging financial ‘products’ from seagrass ecosystem services that can support restoration and creation, require a high degree of integrity. The natural capital asset and risk register framework can provide information to underpin product development. With the development of revenue streams from ecosystem services there is a need for more intentional governance and site-based monitoring of these habitats as natural capital assets. Further research is needed to define any social or economic outcomes. Synthesis and Application By assessing the risk to the status of seagrass assets through this approach, it is possible to determine the complementary governance actions needed to underpin investment in seagrass habitat enhancement. The methods are transferable to other locations where data exists to define the asset status. These specific findings are relevant nationally where similar vectors of risk (pressures) are identified.

Dual-level adaptive incongruity-enhanced model for multimodal sarcasm detection

Multimodal sarcasm detection leverages multimodal information, such as image, text, etc. to identify special instances whose superficial emotional expression is contrary to the actual emotion. Existing methods primarily focused on the incongruity between text and image information for sarcasm detection. Existing sarcasm methods in which the tendency of image encoders to encode similar images into similar vectors, and the introduction of noise in graph-level feature extraction due to negative correlations caused by the accumulation of GAT layers and the lack of representations for non-neighboring nodes. To address these limitations, we propose a Dual-Level Adaptive Incongruity-Enhanced Model (DAIE) to extract the incongruity between the text and image at both token and graph levels. At the token level, we bolster token-level contrastive learning with patch-based reconstructed image to capture common and specific features of images, thereby amplifying incongruities between text and images. At the graph level, we introduce adaptive graph contrast learning, coupled with negative pair similarity weights, to refine the feature representation of the model’s textual and visual graph nodes, while also enhancing the information exchange among neighboring nodes. We conduct experiments using a publicly available sarcasm detection dataset. The results demonstrate the effectiveness of our method, outperforming several state-of-the-art approaches by 3.33% and 4.34% on accuracy and F1 score, respectively.

Empowering Library Services through User‐Centric Analysis of Co‐loan Patterns

ABSTRACTBy examining loan data from public libraries in South Korea, we seek to understand patterns in user borrowing behaviors and explore thematic connections among borrowed books. The subject headings of 55.5 million book sets borrowed by individual users on the same day were analyzed using ITEM2VEC. We have identified 40 subject heading communities through cosine similarity of each subject vector, and we have labeled each community using a large language model. Two prominent communities were identified: Global Modern Literature and Novels and Children's Literature, Fairy Tales, and Folklore. The latter community was associated with a diverse array of subjects, indicating an expansion in children's reading preferences. The study results will be useful for improving collection development and the relevance of book recommendations, as well as for incorporating user information behavior into traditional library material classification schemes.

Research on English–Chinese machine translation shift based on word vector similarity

Research on English–Chinese machine translation shift based on word vector similarity

Word2Vec과 엔트로피를 활용한 연구동향 분석

To understand trends in a specific research field, qualitative literature analysis of major papers published in that field has traditionally been used. Recently, there has been an increasing effort to quantitatively analyze large volumes of literature using text analysis techniques. This study proposes an analysis method utilizing word embedding and entropy. The proposed method is applied to analyze literature in the field of Management Information Systems (MIS). For this purpose, 12,704 papers published in major MIS journals were collected. Word2Vec and annual keyword clustering were applied to 7,813 keywords extracted from these papers, and the research trends in the MIS field were analyzed through vector similarity analysis. Furthermore, research topics were visualized and classified according to their life cycles based on the rate of entropy change and average values. The method proposed in this study is applicable to various research fields and holds significance as a quantitative approach to analyzing large volumes of literature.

Optimizing biomedical information retrieval with a keyword frequency-driven prompt enhancement strategy

BackgroundMining the vast pool of biomedical literature to extract accurate responses and relevant references is challenging due to the domain's interdisciplinary nature, specialized jargon, and continuous evolution. Early natural language processing (NLP) approaches often led to incorrect answers as they failed to comprehend the nuances of natural language. However, transformer models have significantly advanced the field by enabling the creation of large language models (LLMs), enhancing question-answering (QA) tasks. Despite these advances, current LLM-based solutions for specialized domains like biology and biomedicine still struggle to generate up-to-date responses while avoiding “hallucination” or generating plausible but factually incorrect responses.ResultsOur work focuses on enhancing prompts using a retrieval-augmented architecture to guide LLMs in generating meaningful responses for biomedical QA tasks. We evaluated two approaches: one relying on text embedding and vector similarity in a high-dimensional space, and our proposed method, which uses explicit signals in user queries to extract meaningful contexts. For robust evaluation, we tested these methods on 50 specific and challenging questions from diverse biomedical topics, comparing their performance against a baseline model, BM25. Retrieval performance of our method was significantly better than others, achieving a median Precision@10 of 0.95, which indicates the fraction of the top 10 retrieved chunks that are relevant. We used GPT-4, OpenAI's most advanced LLM to maximize the answer quality and manually accessed LLM-generated responses. Our method achieved a median answer quality score of 2.5, surpassing both the baseline model and the text embedding-based approach. We developed a QA bot, WeiseEule (https://github.com/wasimaftab/WeiseEule-LocalHost), which utilizes these methods for comparative analysis and also offers advanced features for review writing and identifying relevant articles for citation.ConclusionsOur findings highlight the importance of prompt enhancement methods that utilize explicit signals in user queries over traditional text embedding-based approaches to improve LLM-generated responses for specialized queries in specialized domains such as biology and biomedicine. By providing users complete control over the information fed into the LLM, our approach addresses some of the major drawbacks of existing web-based chatbots and LLM-based QA systems, including hallucinations and the generation of irrelevant or outdated responses.

Earthquake event knowledge graph construction and reasoning

Efficient decision-making in earthquake emergencies plays a crucial role in ensuring individual safety, protecting personal property, and maintaining societal stability. However, traditional approaches to earthquake emergency decision-making rely on manual analysis or rule-based methods, which often struggle to fully leverage the wealth of information and uncover hidden data connections. Consequently, the efficiency of earthquake emergency decision-making is compromised. To address this issue, this study proposes a method for constructing an earthquake event knowledge graph and utilizing it for decision-making in earthquake emergencies. Firstly, specialized earthquake event knowledge ontology is developed, tailored to the unique characteristics of earthquake event data. Secondly, structured instances of earthquake event knowledge are extracted from text using transfer learning techniques, enabling the construction of the earthquake event knowledge graph. Thirdly, the earthquake event knowledge is represented as multidimensional vectors using knowledge graph representation learning technology. This facilitates the identification of similar earthquake events through inference based on vector similarity computation. In conclusion, the results of a case-based study demonstrate the effectiveness of the proposed method in providing accurate outcomes, facilitating earthquake event matching, enabling the retrieval and reuse of historical earthquake event knowledge, and serving as a valuable reference for earthquake emergency decision-making.

Community-Based Early Warning System Model for Stream Overflow In Barranquilla

Context: This work aims to design and create a community-based early warning model as an alternative for the mitigation of disasters caused by stream overflow in Barranquilla (Colombia). This model is based on contributions from social networks, which are consulted through their API and filtered according to their location. Methods: With the information collected, cleaning and debugging are performed. Then, through natural language processing techniques, the texts are tokenized and vectorized, aiming to find the vector similarity between the processed texts and thus generating a classification. Results: The texts classified as dealing with stream overflow are processed again to obtain a location or assign a default one, in order to for them to be georeferenced in a map that allows associating the risk zone and visualizing it in a web application to monitor and reduce the potential damage to the population. Conclusions: Three classification algorithms were selected (random forest, extra trees, and k-neighbors) to determine the best classifier. These three algorithms exhibited the best performance and R2 regarding the data processed in the regressions. These algorithms were trained, with the k-neighbor algorithm exhibiting the best performance.

An Improved K-Means Algorithm Based on Contour Similarity

The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means algorithm based on contour similarity is proposed to overcome the drawbacks of the traditional k-means algorithm. For the traditional k-means algorithm, which results in local optimality due to the influence of outliers or noisy data and random selection of the initial clustering centers, the Csk-means algorithm overcomes both drawbacks by combining data lattice transformation and dissimilar interpolation. In particular, the Csk-means algorithm employs Fisher optimal partitioning of the similarity vectors between samples for the process of determining the number of clusters. To improve the robustness of the k-means algorithm to the shape of the clusters, the Csk-means algorithm utilizes contour similarity to compute the similarity between samples during the clustering process. Experimental results show that the Csk-means algorithm provides better clustering results than the traditional k-means algorithm and other comparative algorithms.

Study of some components of the influence and formation of blood flow in patients with "slow flow".

"Slow flow" is one very important concept in modern fundamental and clinical biomedicine. Slow coronary flow is indicative of delayed filling of the terminal coronary artery vessels, occurring in the absence of significant coronary stenosis. This group patient of patients exhibits a high incidence of disability and represents a significant financial and material burden for the state and the healthcare system in general. The primary objective of our study was to examine patients with slow coronary flow. We studied the standard parameters recommended by the international health care system (electrocardiography (by Medica QRS-12, Germany), through the electrical activity of a patient's heart by the electrical impulses (beating) of the heart; HC1(Germany); coagulogramma by Coatron M1 (Germany), troponin by AQT 90 (Germany); general blood test we used automatic human counting device HC1(Germany). Also, we investigate the original parameters (non-standard parameters, which we use in this pilot study) that we were first studied for this diagnosis and non-standard parameters. A general blood test showed that patients with slow flow had a higher blood leukocyte count than the control group, but the amount of hemoglobin was normal, the hematocrit was much higher than in the control group, and the platelet count was close to the lower limit of clinical standards.We obtained details of blood flow by coagulation situation, such as prothrombin time, prothrombin index, international normalized ratio, activated partial thromboplastin time, thrombin time, fibrinogen, and rheological properties such as index of erythrocyte aggregability, index of erythrocyte deformability, plasma viscosity, in silico blood rheological index. Blood flow can be considered as a superposition of vortices with similar frequencies and wave vectors that change after bifurcations or other obstacles in the vascular network. These factors together determine the conditions for structuring the flow of moving blood. Disruption or alteration of these factors results in slow flow. It has been found that the speed of blood flow in the coronary arteries depends on changes in the number and function of red blood cells. Slow flow is directly influenced by the aggregation and deformation of red blood cells, their number, and plasma viscosity. Consequently, the rheological status plays a crucial role in determining blood flow and its velocity.

Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation

Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that aggregate explicit social links have shown promising prospects, they are often constrained by the absence of explicit social data and the assumption of homogeneity, thus overlooking variations in social influence and consistency. These limitations hinder semantic expression and recommendation performance. Therefore, we propose a novel framework for social recommendation. First, we design a bipartite network embedding scheme, which learns vertex representations in the embedding space by modeling 1st-order explicit relations and higher-order implicit relations between vertices. Then, the similarity of the embedding vectors is used to extract top-k semantically consistent friends for each user. Next, we design an algorithm to assign a specific influence value to each user. Finally, we combine the top-k friends of the user and their influence values into an ensemble and add it as a regularization term to the rating prediction process of the user to correct the bias. Experiments on three real benchmark datasets show significant improvements in EISF over state-of-the-art methods.

Patch-wise vector quantization for unsupervised medical anomaly detection

Radiography images inherently possess globally consistent structures while exhibiting significant diversity in local anatomical regions, making it challenging to model their normal features through unsupervised anomaly detection. Since unsupervised anomaly detection methods localize anomalies by utilizing discrepancies between learned normal features and input abnormal features, previous studies introduce a memory structure to capture the normal features of radiography images. However, these approaches store extremely localized image segments in their memory, causing the model to represent both normal and pathological features with the stored components. This poses a significant challenge in unsupervised anomaly detection by reducing the disparity between learned features and abnormal features. Furthermore, with the diverse settings in radiography imaging, the above issue is exacerbated: more diversity in the normal images results in stronger representation of pathological features. To resolve the issues above, we propose a novel pathology detection method called Patch-wise Vector Quantization (P-VQ). Unlike the previous methods, P-VQ learns vector-quantized representations of normal “patches” while preserving its spatial information by incorporating vector similarity metric. Furthermore, we introduce a novel method for selecting features in the memory to further enhance the robustness against diverse imaging settings. P-VQ even mitigates the “index collapse” problem of vector quantization by proposing top-k% dropout. Our extensive experiments on the BMAD benchmark demonstrate the superior performance of P-VQ against existing state-of-the-art methods.

Morphological and Genetic Assessment of Invasive Corbicula Lineages in Southern South America: A Case Study in Argentina.

The broad global distribution of freshwater clams belonging to the genus Corbicula is driven by multiple hermaphroditic lineages. These lineages, characterized by shared morphological traits and phenotypic plasticity, pose challenges to morphological identification. Genetic markers, such as the mitochondrial COI gene, play a crucial role in delineating these lineages and their ranges. Morphotypes represent observed phenotypic variations, while lineages are defined based on genetic markers. Here, we comprehensively review Corbicula's distribution in Argentina, discriminate extant lineages based on both morphological and genetic (COI) data, and describe variations in internal and external morphologies using 15 Argentine populations. Genetic analyses identified two mitochondrial lineages: the AR morphotype (FW5 haplotype) and CS morphotype (FW17 haplotype). Strikingly, despite having similar vectors, origins, and invasive stages, Corbicula lineages exhibit virtually segregated distributions. However, mitochondrial haplotypes are found in sympatry mainly in northeastern Argentina where individuals with intermediate morphotypes exist, suggesting the presence of hybrids due to maternal genome retention. These findings contribute to the clarification of the identity and distribution of Corbicula lineages in Argentina, where the genus has been found for over half a century. Similar studies are needed in other areas to better understand the invasion patterns of this successful and adaptable group.

Fine-Grained Entity-Type Completion Based on Neighborhood-Attention and Cartesian–Polar Coordinates Mapping

Entities refer to things that exist objectively, and entity types are concepts abstracted from entities that have the same features or properties. However, the entity types in the knowledge graph are always incomplete. Currently, the main approach for predicting missing entity types is to learn structured representations of entities and types separately, which ignores neighborhood semantic knowledge of the entity. Therefore, this paper proposes the aggregation neighborhood semantics model for type completion (ANSTC), which extracts neighborhood triple features of target entities with two attentional mechanisms. Meanwhile, the spatial mapping module in ANSTC maps entities from Cartesian coordinate to Polar coordinate system, which can map similar vectors onto a concentric circle and then rotate the angle according to the fine-grained difference to achieve entity-to-type transformation. Moreover, we add semantic features from text to the entity representations to enrich semantics. Through experimental comparison on the FB15K and YAGO43K dataset, we get similar results to the baseline. We also construct person dataset in computer domain, and the values of MRR, Hit@1, Hit@3 and Hit@10 are improved compared with the ConnectE model. The experimental results demonstrate that our model can effectively predict the fine-grained entity types in the domain dataset, and achieve state-of-the-art performance.

Clinical nSights: A software platform to accelerate real world oncology analyses.

e23316 Background: Real world data analyses are critical to assess drug safety and effectiveness, including in patient populations underrepresented in clinical trials. Significant barriers to performing such analyses remain, including: (1) incomplete access to electronic health record (EHR) data due to patient privacy concerns, (2) lack of data harmonization within and across EHRs, (3) requirements for time- and labor-intensive data curation efforts, and (4) necessity of coding or data science expertise given the absence of user-friendly platforms. We present Clinical nSights, a software platform that addresses these challenges to enable researchers and clinicians to perform rapid, robust, and scalable real world oncology analyses. Methods: With input EHR data from multiple health systems, we developed intuitive user interfaces and a scalable researcher friendly programming environment for real world clinical analytics. We used (1) nCognito, a previously described HIPAA-compliant de-identification algorithm, to de-identify EHRs across data modalities; (2) the combination of a de novo knowledge graph, vector and distribution similarity metrics, and manual review to harmonize data types including but not limited to laboratory tests, disease codes, and drugs; and (3) a series of Bidirectional Encoder Representations from Transformers (BERT) models to perform augmented curation of variables of interest (e.g., cancer staging, tumor biomarkers) from unstructured clinical records. We developed a user interface for users to create and analyze defined patient cohorts. Results: Clinical nSights facilitates intuitive cohort creation via flexible specification of inclusion and exclusion criteria, including options to perform temporal linking between criteria and propensity score matching. The ability to specify criteria documented in de-identified clinical notes expands the repertoire of available features for cohort creation beyond those captured in structured EHR fields, including but not limited to cancer stage and tumor biomarker status (e.g., HER2 status, microsatellite instability). Data harmonization enables scalable longitudinal analyses of laboratory test data across cancer types, including changes in measurements in the years leading up to and following initial cancer diagnosis. Augmented curation improves the coverage of phenotypes including immune-related toxicities of checkpoint inhibitors and symptoms experienced by patients prior to a cancer diagnosis. Using the no-code interface, users can easily quantify and visualize outcomes such as overall survival and hospitalization rates. Conclusions: Clinical nSights addresses important challenges in the collection, curation, and analysis of real world oncology data. By enabling a broad range of users to rapidly generate and test hypotheses, this platform has the potential to significantly improve care delivery and patient outcomes.

ClusterLP: A novel Cluster-aware Link Prediction model in undirected and directed graphs

Link prediction models endeavor to understand the distribution of links within graphs and forecast the presence of potential links. With the advancements in deep learning, prevailing methods typically strive to acquire low-dimensional representations of nodes in networks, aiming to capture and retain the structure and inherent characteristics of networks. However, the majority of these methods primarily focus on preserving the microscopic structure, such as the first- and second-order proximities of nodes, while largely disregarding the mesoscopic cluster structure, which stands out as one of the network's most prominent features. Following the homophily principle, nodes within the same cluster exhibit greater similarity to each other compared to those from different clusters, suggesting that they should possess analogous vertex representations and higher probabilities of linkage. In this study, we develop a straightforward yet efficient Cluster-aware Link Prediction framework (ClusterLP), with the objective of directly leveraging cluster structures to predict links among nodes with maximum accuracy in both undirected and directed graphs. Specifically, we posit that establishing links between nodes with similar representation vectors and cluster tendencies is more feasible in undirected graphs, whereas nodes in directed graphs are inclined to point towards nodes with akin representation vectors and greater influence. We tailor the implementation of ClusterLP for undirected and directed graphs, respectively, and experimental findings using multiple real-world networks demonstrate the high competitiveness of our models in the realm of link prediction tasks. The code utilized in our implementation is accessible at https://github.com/ZINUX1998/ClusterLP.

Anomalous dispersion, superluminality, and instabilities in two-flavor theories with local non-Hermitian mass mixing

Pseudo-Hermitian field theories possess a global continuous “similarity” symmetry, interconnecting the theories with the same physical particle content and an identical mass spectrum. In their regimes with real spectra, within this family of similarity transformations, there is a map from the non-Hermitian theory to its Hermitian similarity partner. We promote the similarity transformation to a local symmetry, which requires the introduction of a new vector similarity field as a connection in the similarity space of non-Hermitian theories. In the case of non-Hermitian two-flavor scalar or fermion mixing and by virtue of a novel IR/UV mixing effect, the effect of inhomogeneous non-Hermiticity then reveals itself via anomalous dispersion, instabilities, and superluminal group velocities at very high momenta, thus setting an upper bound on the particle momentum propagating through inhomogeneous backgrounds characterized by Lagrangians with non-Hermitian mass matrices. Such a non-Hermitian extension of the Standard Model of particle physics, encoded in a weak inhomogeneity of the non-Hermitian part of the fermion mass matrix, may nevertheless provide us with a low-energy particle spectrum consistent with experimentally observed properties. Published by the American Physical Society 2024

Word Concreteness and Word Originality

Basque is the oldest known spoken language of Western Europe that is not descended from Indo-European. Despite efforts to stop its use from external influences, the Basque people have shown resiliency throughout their history to keep the language alive. However, as is the case with all other languages, Basque has not been immune to influence from neighboring languages; indeed all Basque speakers are bilingual. The question explored here is whether a word’s concreteness using machine translation from Basque to English and English concreteness scores determines if an original Basque word is spoken today. We find that there is a significant difference of concreteness scores between old and contemporary Basque. We also compared old Basque words with common contemporary Basque words in a semantic vector space and found the old words to cluster more tightly than contemporary words suggesting that words with similar semantic vectors are able to retain usage through time.

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new computational method called PMDAGS, which predicts miRNA-disease associations by utilizing graph nonlinear diffusion convolution network and similarities. PMDAGS first calculates miRNA similarity and disease similarity based on miRNA-target interactions, disease-gene associations and known miRNA-disease associations, respectively. Next, we construct the initial feature of each miRNA (disease) by concatenating its final similarity vector with its known association vector. Based on the known miRNA-disease association network and the initial feature vector of each node, we further apply nonlinear diffusion graph convolution network model to extract the feature embedding vectors. Finally, we concatenate the feature embedding vectors of miRNA and disease and input them into a multi-layer perceptron to identify potential miRNA-disease associations. We conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV), and global leave-one-out cross validation (GLOOCV) on HMDD v2.0 and HMDD v3.2. PMDAGS achieves AUCs of 0.9222, 0.9228, and 0.9221 under 5CV, 10CV and GLOOCV on HMDD v2.0, respectively. In addition, PMDAGS also achieves AUC values of 0.9366, 0.9377, and 0.9376 under 5CV, 10CV and GLOOCV on HMDD v3.2, respectively. According to the experimental results, we can conclude that PMDAGS outperforms other compared methods and can effectively predict miRNA-disease associations.