Generative Input: Towards Next-Generation Input Methods Paradigm

Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs).Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Search for non-resonant pair production of Higgs bosons in the bbˉγγb\bar{b}\gamma\gamma final state with the ATLAS detector

Since the discovery of the Higgs boson in 2012, a priority of the ATLAS and CMS Collaborations has been to better understand its properties and couplings, including those fundamental parameters that are related to interactions involving multiple Higgs bosons. A direct probe of the Higgs boson trilinear self-coupling is possible via Higgs boson pair production, making di-Higgs analyses particularly interesting. In addition, HH production via vector boson fusion, the second-leading HH production process, has a unique sensitivity to the interaction between two Higgs bosons and two vector bosons. Furthermore, enhancements to the di-Higgs production rate would point to new physics beyond the Standard Model, making such analyses interesting with the Run 2 dataset. This poster presents a search for non-resonant production of Higgs boson pair in the two bottom quarks plus two photons final state. This final state benefits from the high BR for the H->$b\bar{b}$ decay while the low BR for the H->$\gamma\gamma$ decay is compensated by the excellent photon trigger and resolution, resulting in a clean signature. The data set used corresponds to an integrated luminosity of 140 $fb^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. The analysis strategy is optimised for HH production via the two dominant production modes (gluon-gluon fusion and vector-boson fusion) to probe anomalous values of the trilinear Higgs boson self-coupling modifier $\kappa_{\lambda}$ and of the quartic HHVV (V=W,Z) coupling modifier $\kappa_{2V}$. No excess over the SM background expectations is found and upper limits on the Higgs boson pair production signal strength are set. Constraints on the two coupling parameters $\kappa_{\lambda}$ and $\kappa_{2V}$ will also be presented. The results are further interpreted in an effective field theory framework in terms of constraints on anomalous Higgs boson (self-)interactions

Studies of new Higgs boson interactions through nonresonant HHHH production in the bbˉγγb\bar{b}\gamma\gamma final state in pppp collisions at s\sqrt{s} = 13 TeV with the ATLAS detector

A search for nonresonant Higgs boson pair production in the$b\bar{b}\gamma\gamma$ final state is performed using 140 fb$^{-1}$ ofproton-proton collisions at a centre-of-mass energy of 13 TeV recordedby the ATLAS detector at the CERN Large Hadron Collider.This analysis supersedes and expands upon theprevious nonresonant ATLAS results in this final state based on thesame dataset.The analysis strategy is optimised to probe anomalous values not only of theHiggs ($H$) boson self-coupling modifier $\kappa_\lambda$ but also of the quartic $HHVV$ ($V=W,Z$) coupling modifier $\kappa_{2V}$. No significant excess above the expected background from Standard Model processes is observed. An observed upper limit$\mu_{HH}<4.0$ is set at 95\% confidence level on the Higgs bosonpair production cross-section normalised to its Standard Model prediction.The 95\% confidence intervals for the coupling modifiersare $-1.4< \kappa_\lambda <6.9$ and $-0.5< \kappa_{2V} <2.7$,assuming all other Higgs boson couplingsexcept the one under study are fixed to the Standard Model predictions.The results are then interpreted in the Standard Model effective fieldtheory and Higgs effective field theory frameworks in terms ofconstraints on the couplings of anomalous Higgs boson(self-)interactions

Measurement of Higgs boson mass and width with the ATLAS detector

The mass of the Higgs boson is a fundamental parameter of the Standard Model which can be measured most precisely in its decays to four leptons and two photons, which benefit from excellent mass resolution. This talk presents the most recent measurements by the ATLAS experiment in these two channels, using the full Run 2 dataset of pp collisions at the LHC collected at 13 TeV. The total width of the Higgs boson is an important parameter for Higgs sector phenomenology. It is too small to be measured directly at the LHC, but indirect measurements can be performed using the off-shell Higgs boson production process in the ZZ and WW final states, as well as through interference effects in the diphoton decay channel. This talk presents the most recent indirect width measurements by the ATLAS experiment in the ZZ channel, using the full Run 2 dataset of pp collisions at the LHC collected at 13 TeV

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot–rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems

Effect of Vibration Procedure on Particle Distribution of Cement Paste

Vibration procedures significantly affect the performances of cement-based materials. However, studies on the distribution of certain particles within cement-based materials are limited due to the complexity and difficulty of identifying each specific particle. This paper presents a new method for simulating and quantifying the movements of particles within cement paste through the use of “tagged materials”. By separating the tagged particles from the cement paste after vibration, the distribution of the particles in the cement paste can be calculated statistically. The effect of the vibration time and frequency, fresh behavior, and powder characteristics of cement paste on particle motions are investigated. The results demonstrate that when the vibration exceeds 1800 s, it induces a significant uneven dispersion of microparticles. This effect is more pronounced at low viscosities (200 Hz). Larger and denser particles exhibit greater dispersion. This method provides a valuable tool for investigating the theory of particle motion in cement paste, which is crucial for understanding the influence of vibration on the properties of cement-based materials

Association between obese phenotypes and risk of carotid artery plaque among chinese male railway drivers

Abstract Background China has the world’s highest rail transportation network density, and the prevalence of obesity among railway workers in China is more than twice that of adults in the world. Carotid artery plaque (CAP) is a simple and noninvasive predictor of early atherosclerosis, while the association between different obese phenotypes and CAP risk among Chinese male railway drivers is unclear. Methods This cross-sectional study was performed among 8,645 Chinese male railway drivers. Obese phenotypes were assessed based on the obesity status (the body mass index ≥ 28 kg/m2 as obesity vs. < 28 kg/m2 as non-obesity) and metabolic status (metabolically healthy vs. metabolically unhealthy). Metabolically unhealthy was defined as the presence of at least one dysfunction, including elevated blood pressure, elevated fasting blood glucose, elevated triglyceride, and reduced high-density-lipoprotein cholesterol. Four obese phenotypes were defined based on the body mass index and metabolic status, i.e., metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and metabolically unhealthy non-obesity (MUNO). Multivariable logistic regression was employed to estimate the association between different obese phenotypes and the risk of CAP. Subgroup analysis was performed to examine the variation of the association by age, circadian rhythm disorders, and history of smoking and drinking. Results The prevalence of CAP among male railway drivers in MHO, MUO, MUNO, and MHNO was 8.75%, 18.67%, 17.82%, and 5.36%, respectively. Compared to those with MHNO, an increased risk for CAP was observed among those with MHO (OR = 2.18, 95% CI: 0.82, 5.10), MUO (OR = 1.78, 95% CI:1.44, 2.21), and MUNO (OR = 2.20, 95% CI: 1.67, 2.89). The subgroup analysis showed that both of the metabolically unhealthy groups (MUNO and MUO) aged < 45 years were prone to a higher risk of CAP (for the MUNO group, OR = 4.27, 95% CI:2.71, 7.10; for the MUO group, OR = 4.00, 95%CI: 2.26, 7.17). Conclusion The obese phenotypes are associated with CAP risk in male railway drivers, especially those with metabolically unhealthy conditions aged < 45 years

Health Communication through Chinese Media on E-Cigarette: A Topic Modeling Approach

Background: Electronic cigarettes (e-cigarettes) have been a newsworthy topic in China. E-cigarettes are receiving greater consumer attention due to the rise of the Chinese e-cigarettes industry. In the past decade, e-cigarettes have been widely debated across the media, particularly their identity and their health effects. Objective: this study aims to (1) find the key topics in e-cigarette news and (2) provide suggestions for future media strategies to improve health communication. Method: We collected Chinese e-cigarettes news from 1 November 2015 to 31 October 2020, in the Huike (WiseSearch) database, using &ldquo;e-cigarettes&rdquo; (Chinese: &ldquo;&#30005;&#23376;&#28895;&rdquo;) as the keyword. We used the Jieba package in python to perform the data cleaning process and the Dirichlet allocation (LDA) topic modeling method to generate major themes of the health communication through news content. Main finding: through an analysis of 1584 news articles on e-cigarettes, this paper finds 26 topics covered with 4 themes as regulations and control (n = 475, 30%), minor protection (n = 436, 27.5%), industry activities (n = 404, 25.5%), and health effects (n = 269, 17%). The peak and decline of the number of news articles are affected by time and related regulations. Conclusion: the main themes of Chinese news content on e-cigarettes are regulations and control, and minor protection. Newspapers should shoulder the responsibilities and play an important role in health communication with balanced coverage

Nanostructure of Superlubricating Tribofilm Based on Friction-Induced a-C:H Films under Various Working Conditions: A Review of Solid Lubrication

Diamond-like carbon (DLC) film has gained widespread popularity as a versatile and important solid lubricant material in the field of tribology. Among various types of DLC films, hydrogen-rich DLC (a-C:H) film as a high-performance material has greatly enhanced anti-friction and anti-wear. However, despite its remarkable capabilities, the surface chemical properties and tribological performance of a-C:H film are significantly influenced by the surrounding environment, in special atmospheric conditions. Its super-slip mechanism involves the participation of hydrogen atoms, which can weaken the normal electron number of the outermost layer of a-C:H film. What is more, it is essential to investigate tribofilms in a vacuum or inert gas environment to ascertain the appropriate tribological properties of a-C:H film, which helps in mitigating oxidation effects. When non-doped DLC films are subjected to friction in a dry nitrogen or argon environment, they create sp3-C-rich transfer films on the contact surface, resulting in macroscopic super-slip effects. This paper aims to introduce and discuss the diverse nanostructures of in situ tribofilms in a-C:H film, focusing on the working environment, and explore the prospective application directions of a-C:H film