USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset

Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination. Such analysis challenges models to understand text holistically while also extracting nuanced information. With the rise of Large Language Models(LLMs), new avenues for sentiment analysis have opened. This paper proposes enhancing performance by leveraging the Mutual Reinforcement Effect(MRE) between individual words and the overall text. It delves into how word polarity influences the overarching sentiment of a passage. To support our research, we annotated four novel Sentiment Text Classification and Part of Speech(SCPOS) datasets, building upon existing sentiment classification datasets. Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a 7-billion parameter size. Experimental results revealed that our model surpassed the performance of gpt-3.5-turbo across all four datasets, underscoring the significance of MRE in sentiment analysis.Comment: Model already Open Sourced, Dataset will release soo

Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition

Information extraction(IE) is a crucial subfield within natural language processing. In this study, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance.Comment: Accept in NLDB2023 as Long Pape

Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks

Information extraction(IE) is a crucial subfield within natural language processing. However, for the traditionally segmented approach to sentence classification and Named Entity Recognition, the intricate interactions between these individual subtasks remain largely uninvestigated. In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks. To achieve this, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance. We additionally implemented the SLG framework on single SC task. It yielded superior accuracies compared to the baseline on two distinct Japanese SC datasets. Notably, in the experiment of few-shot learning, SLG framework shows much better performance than fine-tune method. These empirical findings contribute additional evidence to affirm the efficacy of the SLG framework.Comment: 25 pages, 12 figures, 19 tables. arXiv admin note: substantial text overlap with arXiv:2306.1597

Enhancing the Solubility of Calcium Phosphate Ceramics by Calcium Salt Infiltration for the Purpose of Hematopoietic Stem Cell Culturing

The hematopoietic stem cells (HSCs) have been unquestionably important to therapies that involve blood and immune system replacement. However, the in-vitro culture and the expansion of HSCs inhibit their application. This work aims to develop a composite biodegradable 3D scaffold that would simulate key aspects of the in-vivo microenvironment (niche) in which expansion of the hematopoietic stem cells takes place in human bone marrow. Hydroxyapatite (HA) has been chosen as a scaffold material because of its biocompatibility and the ability to create an osteogenic scaffold and thereby simulate trabecular bone that is known to be important to the HSC niche in bone marrow. It is hypothesized that the use of a Ca-rich HA scaffold will create a three dimensional, protective environment for HSCs and further promote their in-vitro expansion by releasing Ca ions into the culture medium. The first part of this study examined the processing of Ca-rich HA and the release of calcium ions into saline over time. The Ca-rich phase was introduced into the HA by an infiltration process and has been shown to release calcium into the culture medium over 42 days. The second part of this study examined the effect of the scaffold material on the fate of human umbilical vein endothelial cells (HUVECS), a well-known endothelial progenitor model. The results showed, for the first time, that at least some HUVEC cells have hematopoietic potential and that the scaffold promoted differentiation down the hematopoietic cell lineage. This is thought to be due to hemangioblast character in the HUVEC cells which is also shared by HSCs. Finally the effects of the scaffold on the in-vitro co-culture of an osteoblast cell line and primary human bone marrow derived HSCs was studied. The infiltrated scaffolds were shown to stimulate the HSC population to differentiate down the hematopoietic lineage and also showed greater potential to differentiate down the HSC lineage in consequent CFU assays

GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect

Information Extraction (IE) stands as a cornerstone in natural language processing, traditionally segmented into distinct sub-tasks. The advent of Large Language Models (LLMs) heralds a paradigm shift, suggesting the feasibility of a singular model addressing multiple IE subtasks. In this vein, we introduce the General Information Extraction Large Language Model (GIELLM), which integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema. This innovation marks the first instance of a model simultaneously handling such a diverse array of IE subtasks. Notably, the GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance in integrated tasks compared to their isolated counterparts. Our experiments demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent evaluation using the novel Text Classification Relation and Event Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in text and word classification. This breakthrough paves the way for most IE subtasks to be subsumed under a singular LLM framework. Specialized fine-tune task-specific models are no longer needed.Comment: 10 pages, 6 figure

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLS

Simulation study of a novel small animal FLASH irradiator (SAFI) with integrated inverse-geometry CT based on circularly distributed kV x-ray sources

Ultra-high dose rate (UHDR) radiotherapy (RT) or FLASH-RT can potentially reduce normal tissue toxicity. A small animal irradiator that can deliver FLASH-RT treatments similar to clinical RT treatments is needed for pre-clinical studies of FLASH-RT. We designed and simulated a novel small animal FLASH irradiator (SAFI) based on distributed x-ray source technology. The SAFI system comprises a distributed x-ray source with 51 focal spots equally distributed on a 20 cm diameter ring, which are used for both FLASH-RT and onboard micro-CT imaging. Monte Carlo simulation was performed to estimate the dosimetric characteristics of the SAFI treatment beams. The maximum dose rate, which is limited by the power density of the tungsten target, was estimated based on finite-element analysis (FEA). The maximum DC electron beam current density is 2.6 mA/m

The miR-33 gene is identified in a marine teleost: a potential role in regulation of LC-PUFA biosynthesis in Siganus canaliculatus

As the first marine teleost demonstrated to have the ability to biosynthesize long-chain polyunsaturated fatty acids (LC-PUFA) from C18PUFA precursors, rabbitfishSiganus canaliculatusprovides a good model for studying the regulatory mechanisms of LC-PUFA biosynthesis in teleosts. Here the potential roles of miR-33 in such regulation were investigated. The miR-33 gene was identified within intron 16 of the gene encoding sterol regulatory element-binding protein 1 (Srebp1), an activator of LC-PUFA biosynthesis. Expression of miR-33 in rabbitfish tissues correlated with that ofsrebp1, while its expression in liver was highly responsive to ambient salinities and PUFA components, factors affecting LC-PUFA biosynthesis. Srebp1 activation promoted the expression of Δ4 and Δ6 Δ5 fatty acyl desaturases (Fad), key enzymes for LC-PUFA biosynthesis, accompanied by elevated miR-33 abundance in rabbitfish hepatocytes. miR-33 overexpression induced the expression of the twofad, but suppressed that of insulin-induced gene 1 (insig1), which encodes a repressor blocking Srebp proteolytic activation and has targeting sites of miR-33. These results indicated that miR-33, cooperating with Srebp1, may be involved in regulation of LC-PUFA biosynthesis by facilitatingfadexpression, probably through targetinginsig1. To our knowledge, this is the first report of the participation of miR-33 in LC-PUFA biosynthesis in vertebrates