Extend Wave Function Collapse to Large-Scale Content Generation

Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games.Comment: This paper is accepted by IEEE Conference on Games 2023 (nomination of the Best Paper Award

Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition

The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to intermediate layers and perform an adaptive layer-matching mechanism trained by meta-optimization. Experiments on image classification and transfer learning to visual recognition tasks show that layer-wise MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that layer-wise MCL can guide the network to generate better feature representations. Our code is publicly avaliable at https://github.com/winycg/L-MCL.Comment: 18 pages, accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI-2023

A Novel Indium-Catalyzed Three-Component Reaction: General and Efficient One-Pot Synthesis of Substituted Pyrroles

A convenient and general approach towards the synthesis of substituted pyrroles from propargylic acetates, silyl enol ethers, and primary amines was described. This novel transformation was catalyzed by indium trichloride in a one-pot synthesis, and high yields of various pyrrole derivatives were obtained.National Natural Science Foundation of China [20772098

Step pyrolysis of N-rich industrial biowastes: Regulatory mechanism of NOx precursor formation via exploring decisive reaction pathways

Step pyrolysis of N-rich industrial biowastes was used to explore decisive reaction pathways and regulatory mechanisms of NOx precursor formation. Three typical ones involving medium-density fiberboard waste (MFW), penicillin mycelia waste (PMW) and sewage sludge (SS) were employed to compare the formation characteristics of NOx precursors during one-step and two-step pyrolysis. Results demonstrated that considerable NH3-N pre-dominated NOx precursors for one-step pyrolysis at low temperatures, depending on primary pyrolysis of labile amide-N/inorganic-N in fuels. Meanwhile, NOx precursors differed in the increment of each species yield while resembled in the total yield of 20-45 wt.% among three samples at high temperatures, due to specific prevailing reaction pathways linking with distinctive amide-N types. Subsequently, compared with one-step pyrolysis uniformly (800 degrees C), by manipulating intensities of reaction pathways at different stages (selecting differential intermediate feedstocks), two-step pyrolysis was capable of minimizing NOx precursor-N yield by 36-43% with a greater impact on HCN-N (75-85%) than NH3-N (9-37%), demonstrating its great capacity on regulating the formation of NOx precursors for industrial biowaste pyrolysis. These observations were beneficial to develop effective insights into N-pollution emission control during their thermal reutilization

Accelerating-particle-deposition Method for Quickly Evaluating Long-term Performance of Fin-and-tube Heat Exchangers

Fin-and-tube heat exchanger is the most commonly used heat exchanger type in air-conditioning systems. In the actual operation of air-conditioning systems, the dust particles involved in the air may partly deposit and form particulate fouling on fins and tubes when the dusty air flows through the heat exchangers. The deposited particles may gradually block the passageway of air flow and occupy the heat transfer area, which results in the continuous increase of air side thermal resistance and the significant deterioration of the heat transfer capacity of heat exchangers during the long-term operation. In order to quickly evaluate the long-term performance of fin-and-tube heat exchangers, an accelerating-particle-deposition method, which is capable of implementing the particle deposition process on the long-running heat exchangers in a short time, is proposed in this study. The idea of the accelerating-particle-deposition method is to employ high concentration dusty air flow through heat exchangers in the accelerated test, and to quickly form the particulate fouling with the same weight as that on long-running heat exchangers under the actual operating environment with low particle concentration. The accelerating factor, which is defined as the ratio of the actual running time to the accelerated testing time, is calculated based on the deposition weight of dust particles. The deposition weight is calculated by the relationship of the impact frequency and deposition probability of dust particles with the particle concentration of dusty air. An experimental apparatus for accelerating the particle deposition process and testing the heat transfer capacity of fin-and-tube heat exchangers is designed. The predicted long-term performances of heat exchangers based on the proposed accelerating-particle-deposition method are compared with the actual performance data of heat exchangers after 5-8 years’ operation published by China Quality Certification Center. The comparison results show that, the predicted results agree well with the actual operation data, and the mean deviation of the heat transfer capacity is within 10%

Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development

Cyclization of samarium diiodide-generated vinyl radicals in 6-(π-exo)-exo-dig mode

Radical cyclization of vinyl iodides in 6-(pi-exo)-exo-dig mode were effected by SmI2 to give exo-cyclic dienes fused to six-membered rings

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after a user click. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when a conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.Comment: This paper has been accepted by AAAI 202

Iron(III) Chloride-catalyzed Nucleophilic Substitution of Propargylic Alcohols: A General and Efficient Approach for the Synthesis of 1,4-Diynes

A wide variety of 1,4-diynes have been constructed via a novel FeCl(3)-catalyzed coupling reaction of propargylic alcohols with alkynylsilanes. This synthetic approach provides a general, efficient, and economical route to 1,4-cliynes.National Natural Science Foundation of China[20772098, 21072159