Weakly supervised POS tagging without disambiguation

Weakly supervised part-of-speech (POS) tagging is to learn to predict the POS tag for a given word in context by making use of partial annotated data instead of the fully tagged corpora. Weakly supervised POS tagging would benefit various natural language processing applications in such languages where tagged corpora are mostly unavailable. In this article, we propose a novel framework for weakly supervised POS tagging based on a dictionary of words with their possible POS tags. In the constrained error-correcting output codes (ECOC)-based approach, a unique L-bit vector is assigned to each POS tag. The set of bitvectors is referred to as a coding matrix with value { 1, -1}. Each column of the coding matrix specifies a dichotomy over the tag space to learn a binary classifier. For each binary classifier, its training data is generated in the following way: each pair of words and its possible POS tags are considered as a positive training example only if the whole set of its possible tags falls into the positive dichotomy specified by the column coding and similarly for negative training examples. Given a word in context, its POS tag is predicted by concatenating the predictive outputs of the L binary classifiers and choosing the tag with the closest distance according to some measure. By incorporating the ECOC strategy, the set of all possible tags for each word is treated as an entirety without the need of performing disambiguation. Moreover, instead of manual feature engineering employed in most previous POS tagging approaches, features for training and testing in the proposed framework are automatically generated using neural language modeling. The proposed framework has been evaluated on three corpora for English, Italian, and Malagasy POS tagging, achieving accuracies of 93.21%, 90.9%, and 84.5% individually, which shows a significant improvement compared to the state-of-the-art approaches

Hidden topic–emotion transition model for multi-level social emotion detection

With the fast development of online social platforms, social emotion detection, focusing on predicting readers’ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection

Analysis of bronchovascular patterns in the left superior division segment to explore the relationship between the descending bronchus and the artery crossing intersegmental planes

BackgroundA comprehensive understanding of the anatomical variations in the pulmonary bronchi and arteries is particularly essential to the implementation of safe and precise left superior division segment (LSDS) segmentectomy. However, no report shows the relationship between the descending bronchus and the artery crossing intersegmental planes. Thus, the purpose of the present study was to analyze the branching pattern of the pulmonary artery and bronchus in LSDS using three-dimensional computed tomography bronchography and angiography (3D-CTBA) and to explore the associated pulmonary anatomical features of the artery crossing intersegmental planes.Materials and methodsThe 3D-CTBA images of 540 cases were retrospectively analyzed. We reviewed the anatomical variations of the LSDS bronchus and artery and assorted them according to different classifications.ResultsAmong all 540 cases of 3D-CTBA, there were 16 cases (44.4%) with lateral subsegmental artery crossing intersegmental planes (AX3a), 20 cases (55.6%) Without AX3a in the descending B3a or B3 type, and 53 cases (10.5%) with AX3a, 451 cases (89.5%) Without AX3a in the Without the descending B3a or B3 type. This illustrated that the AX3a was more common in the descending B3a or B3 type (P < 0.005). Similarly, there were 69 cases (36.1%) with horizontal subsegmental artery crossing intersegmental planes (AX1 + 2c), 122 cases (63.9%) Without AX1 + 2c in the descending B1 + 2c type, and 33 cases (9.5%) with AX1 + 2c, 316 cases (90.5%) Without AX1 + 2c in the Without the descending B1 + 2c type. Combinations of the branching patterns of the AX1 + 2c and the descending B1 + 2c type were significantly dependent (p < 0.005). The combinations of the branching patterns of the AX1 + 2c and the descending B1 + 2c type were frequently observed.ConclusionsThis is the first report to explore the relationship between the descending bronchus and the artery crossing intersegmental planes. In patients with the descending B3a or B3 type, the incidence of the AX3a was increased. Similarly, the incidence of the AX1 + 2c was increased in patients with the descending B1 + 2c type. These findings should be carefully identified when performing an accurate LSDS segmentectomy

[MoO4]2−-templated D4h-symmetric sandwich Ag13 nanocluster coprotected with thiolate and phosphine

Mixed-ligand and anion-templated strategies in constructing metal nanoclusters are intricate and ingenious processes that face challenges to be studied. Herein, we report a cationic [Ag13(MoO4)4(SC6H4iPr)2(dppp)8]3+ (Ag13) nanocluster, which is templated using four [MoO4]2− anions and coprotected by 4-isopropylphenol (iPrC6H4S−) and 1,3-bis (diphenylphosphino) propane (dppp). Two capped (Ag4SC6H4iPr)2 units connect with the middle Ag@Ag4 layer via four [MoO4]2− anion templates to form a three-layer D4h-symmetric structure. An ideal crystallographic fourfold axis passes through the central Ag atom and the S and C atoms of the iPrC6H4S− ligand. The layer stacking generates a nonface-centered cubic (nonFCC) structure. The structure and composition of the Ag13 nanocluster have been fully characterized. In addition, the solid ultraviolet–visible (UV–vis) spectra show that Ag13 is a potential narrow-band-gap semiconductor. The photoluminescence (PL) of orange-yellow-light emission is attributed to ligand-to-metal charge transfer. This work has advanced the research on shell engineering of anionic templates and coprotection to assemble high-symmetric Ag nanoclusters

A novel training program: laparoscopic versus robotic-assisted low anterior resection for rectal cancer can be trained simultaneously

BackgroundCurrent expectations are that surgeons should be technically proficient in minimally invasive low anterior resection (LAR)—both laparoscopic and robotic-assisted surgery. However, methods to effectively train surgeons for both approaches are under-explored. We aimed to compare two different training programs for minimally invasive LAR, focusing on the learning curve and perioperative outcomes of two trainee surgeons.MethodsWe reviewed 272 consecutive patients undergoing laparoscopic or robotic LAR by surgeons A and B, who were novices in conducting minimally invasive colorectal surgery. Surgeon A was trained by first operating on 80 cases by laparoscopy and then 56 cases by robotic-assisted surgery. Surgeon B was trained by simultaneously performing 80 cases by laparoscopy and 56 by robotic-assisted surgery. The cumulative sum (CUSUM) method was used to evaluate the learning curves of operative time and surgical failure.ResultsFor laparoscopic surgery, the CUSUM plots showed a longer learning process for surgeon A than surgeon B (47 vs. 32 cases) for operative time, but a similar trend in surgical failure (23 vs. 19 cases). For robotic surgery, the plots of the two surgeons showed similar trends for both operative times (23 vs. 25 cases) and surgical failure (17 vs. 19 cases). Therefore, the learning curves of surgeons A and B were respectively divided into two phases at the 47th and 32nd cases for laparoscopic surgery and at the 23rd and 25th cases for robotic surgery. The clinicopathological outcomes of the two surgeons were similar in each phase of the learning curve for each surgery.ConclusionsFor surgeons with rich experience in open colorectal resections, simultaneous training for laparoscopic and robotic-assisted LAR of rectal cancer is safe, effective, and associated with accelerated learning curves

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN

Influence of Ag micro-alloying on the thermal stability and ageing characteristics of a Cu–14Fe in-situ composite

This paper studied the influence of Ag micro-alloying on the thermal stability and ageing characteristics of a deformation-processed Cu–14Fe in-situ composite prepared by thermo-mechanical processing. Heat treatment caused (i) edge recession, longitudinal splitting, cylinderization, break-up and spheroidisation of the Fe fibres in the Ag micro-alloyed Cu–14Fe in-situ composite, and (ii) recovery, recrystallisation and precipitation in the Cu matrix. Ag micro-alloying caused these processes to occur at lower temperatures. The index Z (a combination figure of merit that assesses the service performance) reached the peak value of 3.3×10 MPa·% IACS after isothermal heat treatment at 500 °C for 1 h, where IACS is the International Annealed Copper Standard, a measure of conductivity. The optimum combinations of tensile strength and conductivity were 1033 MPa and 56.6% IACS; 931 MPa and 58.9% IACS; or 851 MPa and 60.6% IACS. The tensile strength and conductivity of Ag micro-alloyed Cu–14Fe in-situ composite at η=7.8 after isochronal heat treatments were higher than those of the Cu–14Fe in-situ composite at each temperature

Right upper lobe segmentectomy and subsegmentectomy guided by classification pattern of peripheral segmental veins

BackgroundStudies have analyzed the simplified branching pattern of peripheral segmental veins and developed a standardized approach for intersegmental vein identification in the right upper lobe (RUL). However, the identification approach of intersubsegmental veins has not been reported. This study aimed to supplement the identification approach of intersubsegmental veins and the classification pattern of peripheral segmental veins by using three-dimensional computed tomography bronchography and angiography (3D-CTBA).Materials and methodsA total of 600 patients with ground glass opacity (GGO) who had undergone 3D-CTBA preoperatively at Hebei General Hospital between September 2020 and September 2022 were used for the retrospective study. We reviewed the anatomical variations of RUL veins in these patients using 3D-CTBA images.ResultsAccording to the anatomical position, the peripheral segmental veins structures of RUL were classified into five categories: “Iab type of anterior with central vein” (256/600, 42.7%), “Ib type of anterior with central vein” (166/600, 27.7%), “Central vein type” (38/600, 6.3%), “Anterior vein type” (81/600, 13.5%), “Right top pulmonary vein type” (57/600, 9.5%). The approach for intersegmental vein and intersubsegmental veins identification was divided into five types: anterior approach, posterobronchial approach, central vein approach, V2t approach, and intermediate bronchus posterior surface approach.ConclusionsThe classification pattern of peripheral segmental veins should find wide application. Further, approaches identifying intersegmental veins and intersubsegmental veins may help thoracic surgeons perform safe and accurate RUL segmentectomy