Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation

The rapid proliferation of e-commerce platforms accentuates the need for advanced search and retrieval systems to foster a superior user experience. Central to this endeavor is the precise extraction of product attributes from customer queries, enabling refined search, comparison, and other crucial e-commerce functionalities. Unlike traditional Named Entity Recognition (NER) tasks, e-commerce queries present a unique challenge owing to the intrinsic decorative relationship between product types and attributes. In this study, we propose a pioneering framework that integrates BERT for classification, a Conditional Random Fields (CRFs) layer for attribute value extraction, and Large Language Models (LLMs) for data annotation, significantly advancing attribute recognition from customer inquiries. Our approach capitalizes on the robust representation learning of BERT, synergized with the sequence decoding prowess of CRFs, to adeptly identify and extract attribute values. We introduce a novel decorative relation correction mechanism to further refine the extraction process based on the nuanced relationships between product types and attributes inherent in e-commerce data. Employing LLMs, we annotate additional data to expand the model's grasp and coverage of diverse attributes. Our methodology is rigorously validated on various datasets, including Walmart, BestBuy's e-commerce NER dataset, and the CoNLL dataset, demonstrating substantial improvements in attribute recognition performance. Particularly, the model showcased promising results during a two-month deployment in Walmart's Sponsor Product Search, underscoring its practical utility and effectiveness.Comment: 9 pages, 5 image

Fermentation Parameters, Amino Acids Profile, Biogenic Amines Formation, and Bacterial Community of Ensiled Stylo Treated with Formic Acid or Sugar

Substantial proteolysis occurs and free amino acids can be degraded to biogenic amines by decarboxylation during stylo (Stylosanthes guianensis) ensiling. High biogenic amine concentrations in silage are harmful to the health of ruminant animals. The purposes of this work were to (1) analyze the biogenic amines and amino acids concentrations, bacterial composition, and fermentation profile of spontaneously fermented stylo silage, (2) explore the effect of formic acid or sugar additive on these silage parameters, and (3) further reveal the correlations between silage amines and fermentation parameters, amino acids, and bacteria. Freshly chopped stylo was treated with distilled water (control), formic acid (4 mL/kg), and sugar (20 g/kg) and fermented for 28 days. The results indicated that putrescine (321 mg/kg dry matter), cadaverine (384 mg/kg dry matter), and tyramine (127 mg/kg dry matter) rapidly increased in concentration and become predominant in the control silage after 28 days of fermentation. Applying formic acid and sugar at ensiling, especially the acidifier, significantly decreased putrescine, cadaverine, tyramine, and total biogenic amine concentrations compared with the control treatment (p < 0.0001). Clostridium pabulibutyricum, Weissella cibaria and W. paramesenteroides were the predominant bacteria in the control silage, and the application of both additives remarkably lowered their relative abundance in comparison with the control treatment (p < 0.001). Correlation analysis showed that putrescine, cadaverine, and tyramine were positively related to pH, butyric acid, non-protein nitrogen, and ammonia nitrogen (p < 0.01). These amines also had significant correlations with C. pabulibutyricum, W. cibaria and W. paramesenteroides (p < 0.001). Putrescine, cadaverine, and tyramine were the main biogenic amines and C. pabulibutyricum was the predominant undesirable bacterium in naturally fermented stylo silage. C. pabulibutyricum, W. cibaria and W. paramesenteroides were positively related to putrescine, cadaverine, and tyramine formation. The application of formic acid or sugar significantly reduced the undesirable bacterial population and improved the fermentation and hygienic quality of the stylo silage. These findings lay the foundation for further elucidating the microbial mechanism underlying the main biogenic amine formation during fermentation of stylo silage

A system for the management of clinical tasks throughout the clinical process with notification features

Computer-Interpretable Guidelines have been associated with a higher integration of standard practices in the daily context of health care institutions. The Clinical Decision Support Systems that deliver these machine-interpretable recommendations usually follow a Q & A style of communication, retrieving information from the user or a clinical repository and performing reasoning upon it, based on the rules from Clinical Practice Guidelines. However, these systems are limited in the reach they are capable of achieving as they were initially conceived for use in very specific moments of the clinical process, namely in physician appointments. The purpose of this work is thus to present a system that, in addition to Q & A reasoning, is equipped with other functionalities such as the scheduling and temporal management of clinical tasks, the mapping of these tasks onto an agenda of activities to allow an easy consultation by health care professionals, and notifications that let health care professionals know of task enactment times and information collection times. In this way, the system ensures the delivery of procedures. The main components of the system, which reflect a different perspective on the delivery of CIG advice that we call guideline as a service, are disclosed, and they include a health care Personal Assistant Web Application, a health care assistant mobile application, and the integration with the private calendar services of the user.(POCI-01-0145-)info:eu-repo/semantics/publishedVersio

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging

A Semantic Web Management Model for Integrative Biomedical Informatics

Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MD Anderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis

Development of Large-Scale Functional Brain Networks in Children

Large-scale rewiring of brain circuits in children leads to emergence of hierarchical organization in the mature adult brain

Long-term repetition priming and semantic interference in a lexical-semantic matching task: tapping the links between object names and colors

Using a novel paradigm to engage the long-term mappings between object names and the prototypical colours for objects, we investigated the retrieval of object-colour knowledge as indexed by long-term priming (the benefit in performance from a prior encounter with the same or a similar stimulus); a process about which little is known. We examined priming from object naming on a lexical-semantic matching task. In the matching task participants encountered a visually presented object name (Experiment 1) or object shape (Experiment 2) paired with either a colour patch or colour name. The pairings could either match whereby both were consistent with a familiar object (e.g., strawberry and red) or mismatch (strawberry and blue). We used the matching task to probe knowledge about familiar objects and their colours pre-activated during object naming. In particular, we examined whether the retrieval of object-colour information was modality-specific and whether this influenced priming. Priming varied with the nature of the retrieval process: object-colour priming arose for object names but not object shapes and beneficial effects of priming were observed for colour patches whereas inhibitory priming arose with colour names. These findings have implications for understanding how object knowledge is retrieved from memory and modified by learning