How do tourism goal disclosure motivations drive Chinese tourists\u27 goal-directed behaviors? The influences of feedback valence, affective rumination, and emotional engagement

Based on self-determination theory and the broaden-and-build theory of positive emotion, this study investigated the motivations of disclosing tourism goals on social media and its impacts on Chinese tourists\u27 goal-directed behaviors (GDBs). We proposed and tested a mutual transformation model of tourism goal disclosure motivation under different conditions of feedback valence (positive vs. negative feedback) and examine the mediating role of tourists\u27 affective rumination and emotional engagement. The results revealed that tourists driven by extrinsic motivations develop a stronger emotional engagement in their tourism goals and exhibit more GDBs after receiving positive feedback on their disclosed tourism goals. However, negative feedback disclosed goals lowers GDBs and leads to affective rumination about tourism goals among those with intrinsic motivations. This study provides theoretical and practical implications for destination marketers to adopt marketing strategies based on the findings

GP-NAS-ensemble: a model for NAS Performance Prediction

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance prediction track

Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on pre-trained model weights and discrete time step scenarios, necessitating additional training sessions to achieve their goals. To address these issues, we propose the Catch-Up Distillation (CUD), which encourages the current moment output of the velocity estimation model ``catch up'' with its previous moment output. Specifically, CUD adjusts the original Ordinary Differential Equation (ODE) training objective to align the current moment output with both the ground truth label and the previous moment output, utilizing Runge-Kutta-based multi-step alignment distillation for precise ODE estimation while preventing asynchronous updates. Furthermore, we investigate the design space for CUDs under continuous time-step scenarios and analyze how to determine the suitable strategies. To demonstrate CUD's effectiveness, we conduct thorough ablation and comparison experiments on CIFAR-10, MNIST, and ImageNet-64. On CIFAR-10, we obtain a FID of 2.80 by sampling in 15 steps under one-session training and the new state-of-the-art FID of 3.37 by sampling in one step with additional training. This latter result necessitated only 620k iterations with a batch size of 128, in contrast to Consistency Distillation, which demanded 2100k iterations with a larger batch size of 256. Our code is released at https://anonymous.4open.science/r/Catch-Up-Distillation-E31F

Changes of Circulating Transforming Growth Factor-²1 Level During Radiation Therapy Are Correlated with the Prognosis of Locally Advanced Non-small Cell Lung Cancer

IntroductionWe hypothesized that plasma transforming growth factor-²1 (TGF-²1) level and its dynamic change are correlated with the prognosis of locally advanced non-small cell lung cancer (NSCLC) treated with radiation therapy (RT).MethodsPatients with stage IIIA or IIIB NSCLC treated with RT with or without chemotherapy were eligible for this study. Platelet poor plasma was collected from each patient within 1 week before RT (pre-RT) and at the 4th week during RT (during-RT). TGF-²1 level was measured with enzyme-linked immunosorbent assay. The primary end point was overall survival (OS) and the secondary end point was progression-free survival (PFS). Kaplan-Meier and Cox regression were used for risk factor evaluation.ResultsA total of 65 patients were eligible for the study. The median OS and PFS were 17.7 and 13.7 months, respectively. In univariate analysis, performance status, weight loss, radiation dose, and TGF-²1 ratio (during-RT/pre-RT TGF-²1 level) were all significantly correlated with OS. In the multivariate analysis, performance status, radiation dose, and TGF-²1 ratio were still significantly correlated with OS. The median OS was 30.7 months for patients with TGF-²1 ratio ≤1 versus 13.3 months for those with TGF-²1 ratio more than 1 (p = 0.0029); and the median PFS was 16.8 months versus 7.2 months, respectively (p = 0.010).ConclusionsIn locally advanced NSCLC, the decrease of TGF-²1 level during RT is correlated with favorable prognosis

Scoring System for Tumor-Infiltrating Lymphocytes and Its Prognostic Value for Gastric Cancer

The tumor microenvironment (TME) is the internal environment of malignant tumor progression, and the host antitumor immune response and normal tissue destruction occur in the TME. Tumor-infiltrating lymphocytes (TIL) is a crucial component of the TME and reflect the host antitumor immune response. The purpose of this study was to discuss the methodology for TIL evaluation and assess the prognostic value of TIL in gastric cancer. In total, we reviewed 1,033 gastrectomy cases between 2002 and 2008 at the Third Affiliated Hospital of Soochow University. To understand the prognostic value of TIL in gastric cancer (GC), TIL were assessed by optical microscopy, and verified by immunohistochemistry. There is no current consensus on TIL scoring in GC. In this study, we discussed a TIL evaluation system that includes an analysis of the amount and percentage of TIL in a tumor. Ultimately, 439 (52.7%) cases showed high levels of TIL and 394 (47.3%) cases had low levels. There was a statistically significant relationship among TIL, tumor size, histological grade, LN metastasis, nerve invasion, tumor thrombus, pTN stage, and WHO subtypes (p < 0.001, respectively). TILhi was a positive significant predictor of overall survival (OS) in Kaplan–Meier survival analysis (P < 0.001) and multivariate Cox regression analysis (HR = 0.431, 95% CI: 0.347–0.534, P < 0.001). After surgery, patients with malignant tumors underwent chemoradiotherapy according to standard therapeutic guidelines based on TNM stage. The TNM scoring system cannot reflect the full information of TME; therefore, TIL can be used as a diagnostic supplement. We constructed a nomogram model that showed more predictive accuracy for OS than pTN stage. In summary, this study proves that high levels of TIL are associated with a positive prognosis and that TIL reflect the protective host antitumor immune response

Enhancing text clustering by leveraging Wikipedia semantics

Most traditional text clustering methods are based on “bag of words ” (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved

Topological Supercavity Resonances in the Finite System

Acoustic resonant cavities play a vital role in modern acoustical systems. The ultrahigh quality-factor resonances are highly desired for some applications such as high-resolution acoustic sensors and acoustic lasers. Here, a class of supercavity resonances is theoretically proposed and experimentally demonstrated in a coupled acoustic resonator system, arising from the merged bound states in the continuum (BICs) in geometry space. Their topological origin is demonstrated by explicitly calculating their topological charges before and after BIC merging, accompanied by charges annihilation. Compared with other types of BICs, they are robust to the perturbation brought by fabrication imperfection. Moreover, it is found that such supercavity modes can be linked with the Friedrich-Wintgen BICs supported by an entire rectangular (cuboid) resonator sandwiched between two rectangular (or circular) waveguides and thus more supercavity modes are constructed. Then, these coupled resonators are fabricated and such a unique phenomenon-moving, merging, and vanishing of BICs-is experimentally confirmed by measuring their reflection spectra, which show good agreement with the numerical simulation and theoretical prediction of mode evolution. The results may find exciting applications in acoustic and photonics, such as enhanced acoustic emission, filtering, and sensing

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19