Stationary and moving bright solitons in Bose-Einstein condensates with spin-orbit coupling in a Zeeman field

With the discovery of various matter wave solitons in spin-orbit-coupled Bose-Einstein condensates (BECs), exploring their properties has become increasingly significant. We mainly study stationary and moving bright solitons in spin-orbit-coupled spin-1 BECs with or without a Zeeman field. The bright solitons correspond to the plane wave (PW) and standing wave (SW) phases. With the assistance of single-particle energy spectrum, we obtain the existence domains of PW and SW solitons by analytical and numerical methods. The results indicate that the interaction between atoms is also a key factor determining the existence of solitons. In addition, we systematically discuss the stability domains of PW and SW solitons, and investigate the impact of different parameters on the stability domains. We find that PW solitons are unstable when the linear Zeeman effect reaches a certain threshold, and the threshold is determined by other parameters. The linear Zeeman effect also leads to the alternating distribution of stable and unstable areas of SW solitons, and makes SW solitons stably exist in the area with stronger ferromagnetism. Finally, we analyze the collision dynamics of different types of stable solitons.Comment: 11 pages, 11 figure

OpenAGI: When LLM Meets Domain Experts

Human intelligence has the remarkable ability to assemble basic skills into complex ones so as to solve complex tasks. This ability is equally important for Artificial Intelligence (AI), and thus, we assert that in addition to the development of large, comprehensive intelligent models, it is equally crucial to equip such models with the capability to harness various domain-specific expert models for complex task-solving in the pursuit of Artificial General Intelligence (AGI). Recent developments in Large Language Models (LLMs) have demonstrated remarkable learning and reasoning abilities, making them promising as a controller to select, synthesize, and execute external models to solve complex tasks. In this project, we develop OpenAGI, an open-source AGI research platform, specifically designed to offer complex, multi-step tasks and accompanied by task-specific datasets, evaluation metrics, and a diverse range of extensible models. OpenAGI formulates complex tasks as natural language queries, serving as input to the LLM. The LLM subsequently selects, synthesizes, and executes models provided by OpenAGI to address the task. Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF) mechanism, which uses the task-solving result as feedback to improve the LLM's task-solving ability. Thus, the LLM is responsible for synthesizing various external models for solving complex tasks, while RLTF provides feedback to improve its task-solving ability, enabling a feedback loop for self-improving AI. We believe that the paradigm of LLMs operating various expert models for complex task-solving is a promising approach towards AGI. To facilitate the community's long-term improvement and evaluation of AGI's ability, we open-source the code, benchmark, and evaluation methods of the OpenAGI project at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table

GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset

User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.Comment: Accepted for presentation at 26th European Conference on Artificial Intelligence (ECAI2023

Counterfactual Collaborative Reasoning

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability -- counterfactual reasoning and (neural) logical reasoning -- we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which -- together with the original training examples -- can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations

Constrained Update Projection Approach to Safe Policy Optimization

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and interpretability for some existing algorithms; 3) CUP provides a non-convex implementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at this link https://github.com/zmsn-2077/ CUP-safe-rl.Comment: Accepted by NeurIPS2022. arXiv admin note: substantial text overlap with arXiv:2202.0756

Synthesis of Icariin-Zinc and its Protective Effect on Exercise Fatigue and Reproductive System Related Glands in Male Rats

Background: Icariin, a traditional Chinese medicine, plays a protective role in the treatment of exercise fatigue. Zinc, a trace element, plays an important role in the reproductive system. Therefore, we aimed to synthesize an Icariin-Zinc complex (by chemical means) and verify its protective effect on exercise fatigue and the reproductive system using animal experiments.Methods: The icariin-zinc complex was prepared by the reaction of icariin carbonyl and zinc ions (molar ratio 1:3). The molecular formula and structural formula of the complex were identified and tested. Fifty-six rats selected by swimming training were randomly divided into six groups: static control, exercise control, icariin, gluconate zinc (G-Zn group), icariin glucose zinc and icariin-zinc exercise ( low, high dose/L-E group, H-E group) groups. These groups respectively received the following doses: 1 ml/100 g, daily gavage with NS (for the first two groups), 45 mg/kg icariin, 110 mg/kg Gluconate Zinc, Icariin glucose zinc (45 mg/kg Icariin and 110 mg/kg Gluconate Zinc), 60 mg/kg icariin zinc and 180 mg/kg icariin zinc. After 3 weeks of gavage, we conducted 6 weeks of exhaustive swimming training. Test indices such as exhaustive swimming time of rats and body weight were evaluated after the last training exercise. The seminal vesicles, testes, and prostate gland were weighed, and their indices were calculated. The levels of testosterone (in the plasma) and glycogen (in the liver and muscle homogenates) were also evaluated using ELISA.Results: Compared with the static control group, the exhaustive swimming time of the rats in each group was prolonged. Compared with the other groups, the exhaustive swimming time of the L-E and H-E groups was significantly longer (p < 0.01); the Icariin-Zinc complex significantly increased the exhaustive swimming time of the rats. Compared with the static control group, the plasma testosterone content of the L-E and H-E groups increased significantly (p < 0.05). Compared with the exercise control group and G-Zn group, the plasma testosterone content of the H-E group also increased significantly (p < 0.01). The Icariin-Zinc complex significantly increased the serum levels of testosterone in rats. Compared with the control group, the muscle glycogen reserves of each group decreased, indicating that the muscle glycogen reserves of the rats decreased after swimming. Compared with other groups, the Icariin-Zinc complex can reduce the level of glycogen in the muscles, indicating that it can increase the utilization efficiency of glycogen in muscles. Compared with the static control and exercise control groups, the testicular weight of rats in the administration groups increased slightly. The Icariin-Zinc complex increased the testicular weight, indicating that the function of the reproductive system was improved to some extent.Conclusion: Icariin-Zinc can significantly prolong the exhaustive swimming time, improve exercise ability, and increase the plasma testosterone level (which is beneficial for improving the reproductive ability of male rats). Moreover, the beneficial effect of Icariin-Zinc on the glycogen content, testis index, and other reproductive system glands is dose-dependent

Generalist taxa shape fungal community structure in cropping ecosystems

Fungi regulate nutrient cycling, decomposition, symbiosis, and pathogenicity in cropland soils. However, the relative importance of generalist and specialist taxa in structuring soil fungal community remains largely unresolved. We hypothesized that generalist fungi, which are adaptable to various environmental conditions, could potentially dominate the community and become the basis for fungal coexisting networks in cropping systems. In this study, we identified the generalist and habitat specialist fungi in cropland soils across a 2,200 kms environmental gradient, including three bioclimatic regions (subtropical, warm temperate, and temperate). A few fungal taxa in our database were classified as generalist taxa (~1%). These generalists accounted for >35% of the relative abundance of all fungal populations, and most of them are Ascomycota and potentially pathotrophic. Compared to the specialist taxa (5–17% of all phylotypes in three regions), generalists had a higher degree of connectivity and were often identified as hub within the network. Structural equation modeling provided further evidence that after accounting for spatial and climatic/ edaphic factors, generalists had larger contributions to the fungal coexistence pattern than habitat specialists. Taken together, our study provided evidence that generalist taxa are crucial components for fungal community structure. The knowledge of generalists can provide important implication for understanding the ecological preference of fungal groups in cropland systems