GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models

Offline goal-conditioned RL (GCRL) offers a feasible paradigm to learn general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods have been restricted to model-free approaches, constraining their capacity to tackle limited data budgets and unseen goal generalization. In this work, we propose a novel two-stage model-based framework, Goal-conditioned Offline Planning (GOPlan), including (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, the prior policy is based on an advantage-weighted Conditioned Generative Adversarial Networks that exhibits distinct mode separation to overcome the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. Through experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals.Comment: Spotlight Presentation at Goal-conditioned Reinforcement Learning Workshop at NeurIPS, 202

Poly[tetra­kis(μ-cyclo­hexa­ne-1,4-di­carboxyl­ato)di-μ-hydroxido-penta­zinc(II)]

In the title coordination polymer, [Zn5(μ3-OH)2(1,4-CDC)4]n (1,4-CDCH2 = 1,4-cyclo­hexa­nedicarboxylic acid) or [Zn5(C8H10O4)4(OH)2]n, the asymmetric unit comprises one half of an octa­hedrally coordinated ZnO6 complex unit (site symmetry ) and two five-coordinate ZnO5 complex units, together with two μ3-bridging hydroxido ligands and four 1,4-CDC ligands (comprising two whole mol­ecules and four inversion-related half-molecules). The ZnO6 unit consists of four carboxyl­ate O donors (two bridging) and two hydroxido O donors (both bridging three Zn centres) [Zn—O range 2.065 (3)–2.125 (3) Å]. Each of the ZnO5 units [one capped tetra­hedral, the other square-pyrimidal; Zn—O range 1.928 (3)–2.338 (3) Å] has one hydroxido O donor and four carboxyl O donors from three different 1,4-CDC carboxyl­ate O donors (one bridging). Infinite (ZnO)n inorganic chains run parallel to the a axis and are interconnected by the organic ligands into a three-dimensional structure

The Future of ChatGPT-enabled Labor Market: A Preliminary Study

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills

WeChat Adoption among Older Adults and Urban-Rural Differences in China

At the intersection of social digitization and population aging, the challenge of older adults fitting into digital life is becoming more prominent. To understand how to help older adults adopt digital life, this study builds upon the Technology Acceptance Model (TAM) and developed the Digital Technology Motivation Interaction Model (DTMIM) to study the complex effects of autonomous motivation (perceived usefulness, perceived ease of use, perceived enjoyment), controlled motivation, and digital feedback on WeChat adoption among older adults, as well as the urban-rural differences. The results of the questionnaire survey and Fuzzy-set Qualitative Comparative Analysis (fsQCA) show that: First, no single construct is a necessary condition for a high (non-high) attitude toward using (ATU) or high (non-high) actual using (AU). Second, we identified two configurations that trigger high ATU including the autonomy-motivation type and digital feedback under motivational synergy, and three configurations that enable high AU including motivational synergy type, digital feedback under autonomous extrinsic motivation, and digital feedback under motivational synergy. Third, the configurations of high ATU and high AU show significant differences between urban and rural areas. Autonomy motivation plays a universal role in urban older adults’ WeChat adoption, while digital feedback is critical for rural older adults. The configuration analysis of DTMIM and urban-rural differences is not only an adaptive improvement of TAM but also provides new methods and perspectives for future research on the adoption of digital technology

iQIST : An open source continuous-time quantum Monte Carlo impurity solver toolkit

Quantum impurity solvers have a broad range of applications in theoretical studies of strongly correlated electron systems. Especially, they play a key role in dynamical mean-field theory calculations of correlated lattice models and realistic materials. Therefore, the development and implementation of efficient quantum impurity solvers is an important task. In this paper, we present an open source interacting quantum impurity solver toolkit (dubbed iQIST). This package contains several highly optimized quantum impurity solvers which are based on the hybridization expansion continuous-time quantum Monte Carlo algorithm, as well as some essential pre- and post-processing tools. We first introduce the basic principle of continuous-time quantum Monte Carlo algorithm and then discuss the implementation details and optimization strategies. The software framework, major features, and installation procedure for iQIST are also explained. Finally, several simple tutorials are presented in order to demonstrate the usage and power of iQIST

Precise measurement of position and attitude based on convolutional neural network and visual correspondence relationship

Accurate measurement of position and attitude information is particularly important. Traditional measurement methods generally require high-precision measurement equipment for analysis, leading to high costs and limited applicability. Vision-based measurement schemes need to solve complex visual relationships. With the extensive development of neural networks in related fields, it has become possible to apply them to the object position and attitude. In this paper, we propose an object pose measurement scheme based on convolutional neural network and we have successfully implemented end-toend position and attitude detection. Furthermore, to effectively expand the measurement range and reduce the number of training samples, we demonstrated the independence of objects in each dimension and proposed subadded training programs. At the same time, we generated generating image encoder to guarantee the detection performance of the training model in practical applications

Automatic Insertion of Hot Keywords to Drive Traffic on Advertisements

Product titles and descriptions that include appropriate keywords, when used in an online advertisement, can improve the shopping feed quality and resultant traffic to the advertiser. However, online merchants lack knowledge of currently trending or popular keywords, and lacking keyword ideation, may choose suboptimal product titles. This disclosure describes techniques that enable online merchants to automatically optimize product titles or descriptions, e.g., as used in online ads. Trending or popular keywords relevant to the product are automatically added to the product title or description. Unique, product-specific insights gleaned from searched terms are utilized to improve title effectiveness automatically and at scale

New Insights into PI3K Inhibitor Design using X-ray Structures of PI3Kα Complexed with a Potent Lead Compound

Abstract Phosphatidylinositol 3-kinase α is an attractive target to potentially treat a range of cancers. Herein, we described the evolution of a reported PI3K inhibitor into a moderate PI3Kα inhibitor with a low molecular weight. We used X-ray crystallography to describe the accurate binding mode of the compound YXY-4F. A comparison of the p110α–YXY-4F and apo p110α complexes showed that YXY-4F induced additional space by promoting a flexible conformational change in residues Ser773 and Ser774 in the PI3Kα ATP catalytic site. Specifically, residue 773(S) in PI3Kα is quite different from that of PI3Kβ (D), γ (A), and δ (D), which might guide further optimization of substituents around the NH group and phenyl group to improve the selectivity and potency of PI3Kα

Optimization of Fermentation Medium for the Production of Atrazine Degrading Strain Acinetobacter

Statistical experimental designs provided by statistical analysis system (SAS) software were applied to optimize the fermentation medium composition for the production of atrazine-degrading Acinetobacter sp. DNS32 in shake-flask cultures. A “Plackett-Burman Design” was employed to evaluate the effects of different components in the medium. The concentrations of corn flour, soybean flour, and K2HPO4 were found to significantly influence Acinetobacter sp. DNS32 production. The steepest ascent method was employed to determine the optimal regions of these three significant factors. Then, these three factors were optimized using central composite design of “response surface methodology.” The optimized fermentation medium composition was composed as follows (g/L): corn flour 39.49, soybean flour 25.64, CaCO3 3, K2HPO4 3.27, MgSO4 ·7H2O 0.2, and NaCl 0.2. The predicted and verifiable values in the medium with optimized concentration of components in shake flasks experiments were 7.079×108 CFU/mL and 7.194×108 CFU/mL, respectively. The validated model can precisely predict the growth of atrazine-degraing bacterium, Acinetobacter sp. DNS32