Quantum algebra of multiparameter Manin matrices

Multiparametric quantum semigroups $\mathrm{M}_{\hat{q}, \hat{p}}(n)$ are generalization of the one-parameter general linear semigroups $\mathrm{M}_q(n)$, where $\hat{q}=(q_{ij})$ and $\hat{p}=(p_{ij})$ are $2n^2$ parameters satisfying certain conditions. In this paper, we study the algebra of multiparametric Manin matrices using the R-matrix method. The systematic approach enables us to obtain several classical identities such as Muir identities, Newton's identities, Capelli-type identities, Cauchy-Binet's identity both for determinant and permanent as well as a rigorous proof of the MacMahon master equation for the quantum algebra of multiparametric Manin matrices. Some of the generalized identities are also generalized to multiparameter $q$-Yangians.Comment: 31 pages; final versio

PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model

Poker, also known as Texas Hold'em, has always been a typical research target within imperfect information games (IIGs). IIGs have long served as a measure of artificial intelligence (AI) development. Representative prior works, such as DeepStack and Libratus heavily rely on counterfactual regret minimization (CFR) to tackle heads-up no-limit Poker. However, it is challenging for subsequent researchers to learn CFR from previous models and apply it to other real-world applications due to the expensive computational cost of CFR iterations. Additionally, CFR is difficult to apply to multi-player games due to the exponential growth of the game tree size. In this work, we introduce PokerGPT, an end-to-end solver for playing Texas Hold'em with arbitrary number of players and gaining high win rates, established on a lightweight large language model (LLM). PokerGPT only requires simple textual information of Poker games for generating decision-making advice, thus guaranteeing the convenient interaction between AI and humans. We mainly transform a set of textual records acquired from real games into prompts, and use them to fine-tune a lightweight pre-trained LLM using reinforcement learning human feedback technique. To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques. Through the experiments, we demonstrate that PokerGPT outperforms previous approaches in terms of win rate, model size, training time, and response speed, indicating the great potential of LLMs in solving IIGs

Selective Determination of Pyridine Alkaloids in Tobacco by PFTBA Ions/Analyte Molecule Reaction Ionization Ion Trap Mass Spectrometry

The application of perfluorotributylamine (PFTBA) ions/analyte molecule reaction ionization for the selective determination of tobacco pyridine alkaloids by ion trap mass spectrometry (IT-MS) is reported. The main three PFTBA ions (CF3+, C3F5+, and C5F10N+) are generated in the external source and then introduced into ion trap for reaction with analytes. Because the existence of the tertiary nitrogen atom in the pyridine makes it possible for PFTBA ions to react smoothly with pyridine and forms adduct ions, pyridine alkaloids in tobacco were selectively ionized and formed quasi-molecular ion [M + H]+and adduct ions, including [M + 69]+, [M + 131]+, and [M + 264]+, in IT-MS. These ions had distinct abundances and were regarded as the diagnostic ions of each tobacco pyridine alkaloid for quantitative analysis in selected-ion monitoring mode. Results show that the limit of detection is 0.2 μg/mL, and the relative standard deviations for the seven alkaloids are in the range of 0.71% to 6.8%, and good recovery of 95.6% and 97.2%. The proposed method provides substantially greater selectivity and sensitivity compared with the conventional approach and offers an alternative approach for analysis of tobacco alkaloids

Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%

Monitoring Enzyme Reaction and Screening of Inhibitors of Acetylcholinesterase by Quantitative Matrix-Assisted Laser Desorption/Ionization Fourier Transform Mass Spectrometry

A matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS)–based assay was developed for kinetic measurements and inhibitor screening of acetylcholinesterase. Here, FTMS coupled to MALDI was applied to quantitative analysis of choline using the ratio of choline/acetylcholine without the use of additional internal standard, which simplified the experiment. The Michaelis constant (Km) of acetylcholinesterase (AChE) was determined to be 73.9 μmol L−1 by this approach. For Huperzine A, the linear mixed inhibition of AChE reflected the presence of competitive and noncompetitive components. The half maximal inhibitory concentration (IC50) value of galantamine obtained for AChE was 2.39 μmol L−1. Inhibitory potentials of Rhizoma Coptidis extracts were identified with the present method. In light of the results the referred extracts as a whole showed inhibitory action against AChE. The use of high-resolution FTMS largely eliminated the interference with the determination of ACh and Ch, produced by the low-mass compounds of chemical libraries for inhibitor screening. The excellent correlation with the reported kinetic parameters confirms that the MS-based assay is both accurate and precise for determining kinetic constants and for identifying enzyme inhibitors. The obvious advantages were demonstrated for quantitative analysis and also high-throughput characterization. This study offers a perspective into the utility of MALDI-FTMS as an alternate quantitative tool for inhibitor screening of AChE

Impacts of Future Climate Change on Net Primary Productivity of Grassland in Inner Mongolia, China

Net Primary Productivity (NPP) of grassland is a key variable of terrestrial ecosystems and is an important parameter for characterizing carbon cycles in grassland ecosystems. In this research, the Inner Mongolia grassland NPP was calculated using the Miami Model and the impact of climate change on grassland NPP was subsequently analyzed under the Special Report on Emissions Scenarios (SRES) A2, B2, and A1B scenarios, which are inferred from Providing Regional Climates for Impacts Studies (PRECIS) climate model system. The results showed that: (1) the NPP associated with these three scenarios had a similar distribution in Inner Mongolia: the grassland NPP increased gradually from the western region, with less than 200 g/m2/yr, to the southeast region, with more than 800 g/m2/yr. Precipitation was the main factor determining the grassland NPP; (2) compared with the baseline (1961-1990), there would be an overall increase in grassland NPP during three time periods (2020s: 2011-2040, 2050s: 2041-2070, and 2080s: 2071-2100) under the A2 and B2 scenarios; (3) under the A1B scenario, there will be a decreasing trend at middle-west region during the 2020s and 2050s; while there will be a very significant decrease from the 2050s to 2080s for middle Inner Mongolia; and (4) grassland NPP under the A1B scenario would present the most significant increase among the three scenarios, and would have the least significant increase under the B2 scenario

Favoritism Toward Foreign and Domestic Brands: A Comparison of Different Theoretical Explanations

Five theoretical approaches can predict favoritism toward domestic and foreign brands. This article applies a contrastive perspective to examine social identity, personal identity, cultural identity, system justification, and categorical cognition theories and their attendant constructs. The authors propose a set of main-effects hypotheses as well as hypotheses related to both product and country moderation effects on attitudes toward and loyalty to domestic and foreign brands. They test the hypotheses on a sample of Chinese consumers with respect to salient brands from 12 product categories. The results indicate that three of the theoretical approaches examined can explain only one side of favoritism—most commonly favoritism toward domestic brands—but not favoritism toward both domestic and foreign brands. Consumer xenocentrism, a concept rooted in system justification theory, seems to provide more consistent predictions for both domestic- and foreign-brand bias

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors

espite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box adversarial attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, an Explainable Transfer-based Black-Box Adversarial Attack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML models and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints

Learning task-oriented dexterous grasping from human knowledge

Industrial automation requires robot dexterity to automate many processes such as product assembling, packaging, and material handling. The existing robotic systems lack the capability to determining proper grasp strategies in the context of object affordances and task designations. In this paper, a framework of task-oriented dexterous grasping is proposed to learn grasp knowledge from human experience and to deploy the grasp strategies while adapting to grasp context. Grasp topology is defined and grasp strategies are learned from an established dataset for task-oriented dexterous manipulation. To adapt to various grasp context, a reinforcement-learning based grasping policy was implement to deploy different task-oriented strategies. The performances of the system was evaluated in a simulated grasping environment by using an AR10 anthropomorphic hand installed in a Sawyer robotic arm. The proposed framework achieved a hit rate of 100% for grasp strategies and an overall top-3 match rate of 95.6%. The success rate of grasping was 85.6% during 2700 grasping experiments for manipulation tasks given in natural-language instructions