Cryptocurrency price prediction based on multiple market sentiment

With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult. To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions

Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction

Influences of intergrowth structure construction on the structural and electrical properties of the BBT-BiT ceramics

Bismuth Layer Structured Ferroelectrics (BLSFs) have always been an important research direction of high Curie temperature piezoelectrical ceramics, and the construction of intergrowth structure has been considered as an effective method to improve the electric properties of BLSFs. There are many literatures about intergrowth structure improving electrical performance, but few reports analyze the influence of the construction of intergrowth structure on the internal defects and electrical properties in BLSFs. In this study, (1-x) BaBi4Ti4O15 - x Bi4Ti3O12 ceramic samples with intergrowth bismuth layer structure were fabricated by a conventional solid-state reaction method, and the mechanism of the influence of intergrowth structure construction on the structure and electrical properties of BLSFs has been discussed. The crystal structure, phase composition, microstructure, dielectric and piezoelectric performance, relaxation behavior and AC conductivity of ceramic samples were systematically investigated. It has been found that the construction of intergrowth structure can significantly inhibit the generation of oxygen vacancies. The concentration of the oxygen vacancies plays an important role, and its reduction will lead to the inhibition of grain growth and the increase of the relaxation activation energy of ceramics. In addition, the intergrowth structure construction also affects the symmetry of ceramics in the c-axis direction, thus affecting the electrical properties of ceramics

Policy Optimization for Markov Games: Unified Framework and Faster Convergence

This paper studies policy optimization algorithms for multi-agent reinforcement learning. We begin by proposing an algorithm framework for two-player zero-sum Markov Games in the full-information setting, where each iteration consists of a policy update step at each state using a certain matrix game algorithm, and a value update step with a certain learning rate. This framework unifies many existing and new policy optimization algorithms. We show that the state-wise average policy of this algorithm converges to an approximate Nash equilibrium (NE) of the game, as long as the matrix game algorithms achieve low weighted regret at each state, with respect to weights determined by the speed of the value updates. Next, we show that this framework instantiated with the Optimistic Follow-The-Regularized-Leader (OFTRL) algorithm at each state (and smooth value updates) can find an $\mathcal{\widetilde{O}}(T^{-5/6})$ approximate NE in $T$ iterations, and a similar algorithm with slightly modified value update rule achieves a faster $\mathcal{\widetilde{O}}(T^{-1})$ convergence rate. These improve over the current best $\mathcal{\widetilde{O}}(T^{-1/2})$ rate of symmetric policy optimization type algorithms. We also extend this algorithm to multi-player general-sum Markov Games and show an $\mathcal{\widetilde{O}}(T^{-3/4})$ convergence rate to Coarse Correlated Equilibria (CCE). Finally, we provide a numerical example to verify our theory and investigate the importance of smooth value updates, and find that using "eager" value updates instead (equivalent to the independent natural policy gradient algorithm) may significantly slow down the convergence, even on a simple game with $H=2$ layers

Spin triplet superconducting pairing in doped MoS2_2

Searching for triplet superconductivity has been pursued intensively in a broad field of material science and quantum information for decades. Nevertheless, these novel states remain rare. Within a simplified effective three-orbital model, we reveal a spin triplet pairing in doped MoS$_2$ by employing both the finite temperature determinant quantum Monte Carlo approach and the ground state constrained-phase quantum Monte Carlo method. In a wide filling region of \avg{n}=0.60-0.80 around charge neutrality \avg{n}=2/3, the $f$-wave pairing dominates over other symmetries. The pairing susceptibility strongly increases as the on-site Coulomb interaction increases, and it is insensitive to spin-orbit coupling.Comment: Accepted for publication as a Regular Article in Physical Review

The value of combined detection of LDHA and PD-L1 in predicting the efficacy and prognosis of advanced gastric cancer patients treated with PD-1 inhibitor

Background and purpose: The response rate of gastric cancer patients to programmed death-1 (PD-1) inhibitor is relatively low. Establishing a useful efficacy prediction method to screen the superior gastric cancer patients receiving anti-PD-1 therapy could improve the prognosis of patients. This study aimed to explore the value of combined detection of lactate dehydrogenase (LDHA) and programmed death ligand-1 (PD-L1) expressions in predicting the efficacy and prognosis of gastric cancer patients treated with PD-1 inhibitor. Methods: The clinicopathological data of 50 advanced gastric cancer patients treated with PD-1 inhibitor in The First Affiliated Hospital of Wannan Medical College from January 2020 to March 2022 were retrospectively analyzed. The independent risk factors affecting the efficacy of PD-1 inhibitor were analyzed by multivariate logistic regression. The value of combined detection of LDHA and PD-L1 in predicting the efficacy of PD-1 inhibitors in gastric cancer was analyzed by receiver operating characteristic (ROC) curve analysis. Gastric cancer patient survival was analyzed by Kaplan-Meier method. Results: The objective response rate (ORR) of gastric cancer patients receiving PD-1 inhibitor therapy in LDHA low and high expression groups were 59% and 10%, respectively. The disease control rate (DCR) in LDHA low and high expression groups were 83% and 29%, respectively. The difference was statistically significant (P<0.001). Multivariate logistic regression analysis showed that PD-L1 combined positive score (CPS)<5 and LDHA high expression were independent risk factors affecting the efficacy of PD-1 inhibitor in gastric cancer (P<0.05). ROC curve analysis showed that combined detection of LDHA and PD-L1 had good predictive value for the efficacy of PD-1 inhibitor in gastric cancer [area under curve (AUC) was 0.951]. Kaplan-Meier survival analysis showed that gastric cancer patients with low LDHA expression and PD-L1 CPS≥5 had longer overall survival (OS, P=0.003) and progression-free survival (PFS, P<0.001) after receiving PD-1 inhibitor therapy. Conclusion: Low LDHA expression and PD-L1 CPS≥5 were positively correlated with the efficacy of PD-1 inhibitor in gastric cancer. Gastric cancer patients with low LDHA expression and PD-L1 CPS≥5 significantly had prolonged OS and PFS after receiving PD-1 therapy. Therefore, the combined detection of LDHA and PD-L1 expressions has good value in predicting the efficacy and evaluating prognosis of advanced gastric cancer patients treated with PD-1 inhibitor