Beta Diffusion

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them

Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models

Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score function at the patch level, where the patch location in the original image is included as additional coordinate channels, while the patch size is randomized and diversified throughout training to encode the cross-region dependency at multiple scales. Sampling with our method is as easy as in the original diffusion model. Through Patch Diffusion, we could achieve $\mathbf{\ge 2\times}$ faster training, while maintaining comparable or better generation quality. Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, $e.g.$, as few as 5,000 images to train from scratch. We achieve outstanding FID scores in line with state-of-the-art benchmarks: 1.77 on CelebA-64$\times$64, 1.93 on AFHQv2-Wild-64$\times$64, and 2.72 on ImageNet-256$\times$256. We share our code and pre-trained models at https://github.com/Zhendong-Wang/Patch-Diffusion

Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling

Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these computational challenges, a less-explored issue is designing an efficient and adaptable network backbone for iterative refinement. Current options like U-Net and Vision Transformer often rely on resource-intensive deep networks and lack the flexibility needed for generating images at variable resolutions or with a smaller network than used in training. This study introduces LEGO bricks, which seamlessly integrate Local-feature Enrichment and Global-content Orchestration. These bricks can be stacked to create a test-time reconfigurable diffusion backbone, allowing selective skipping of bricks to reduce sampling costs and generate higher-resolution images than the training data. LEGO bricks enrich local regions with an MLP and transform them using a Transformer block while maintaining a consistent full-resolution image across all bricks. Experimental results demonstrate that LEGO bricks enhance training efficiency, expedite convergence, and facilitate variable-resolution image generation while maintaining strong generative performance. Moreover, LEGO significantly reduces sampling time compared to other methods, establishing it as a valuable enhancement for diffusion models

A Review of the Engineering Role of Burrowing Animals: Implication of Chinese Pangolin as an Ecosystem Engineer

Ecosystem engineers are organisms that alter the distribution of resources in the environment by creating, modifying, maintaining and/or destroying the habitat. They can affect the structure and function of the whole ecosystem furthermore. Burrowing engineers are an important group in ecosystem engineers as they play a critical role in soil translocation and habitat creation in various types of environment.However, few researchers have systematically summarized and analyzed the studies of burrowing engineers. We reviewing the existing ecological studies of burrowing engineer about their interaction with habitat through five directions: (1) soil turnover; (2)changing soil physicochemical properties; (3) changing plant community structure; (4) providing limited resources for commensal animals;and/or (5) affecting animal communities. The Chinese pangolin (Manis pentadactyla) is a typical example of burrowing mammals, in part (5), we focus on the interspecific relationships among burrow commensal species of Chinese pangolin. The engineering effects vary with environmental gradient, literature indicates that burrowing engineer play a stronger role in habitat transformation in the tropical and subtropical areas.The most common experiment method is comparative measurements (include different spatial and temporal scale),manipulative experiment is relatively few. We found that most of the engineering effects had positive feedback to the local ecosystem, increased plant abundance and resilience, increased biodiversity and consequently improved ecosystem functioning. With the global background of dramatic climate change and biodiversity loss in recent decades, we recommend future studies should improving knowledge of long-term engineering effects on population scale and landscape scale, exploring ecological cascades through trophic and engineering pathways, to better understand the attribute of the burrowing behavior of engineers to restore ecosystems and habitat creation. The review is presented as an aid to systematically expound the engineering effect of burrowing animals in the ecosystem, and provided new ideas and advice for planning and implementing conservation management

A Regularized Implicit Policy for Offline Reinforcement Learning

Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. We further propose a simple modification to the classical policy-matching methods for regularizing with respect to the dual form of the Jensen--Shannon divergence and the integral probability metrics. We theoretically show the correctness of the policy-matching approach, and the correctness and a good finite-sample property of our modification. An effective instantiation of our framework through the GAN structure is provided, together with techniques to explicitly smooth the state-action mapping for robust generalization beyond the static dataset. Extensive experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs

Fine‐Tuning X‐Ray Sensitivity in Organic–Inorganic Hybrids via an Unprecedented Mixed‐Ligand Strategy

Abstract Crystalline organic–inorganic hybrids, which exhibit colorimetric responses to ionizing radiation, have recently been recognized as promising alternatives to conventional X‐ray dosimeters. However, X‐ray‐responsive organic–inorganic hybrids are scarce and the strategy to fine‐tune their detection sensitivity remains elusive. Herein, an unprecedented mixed‐ligand strategy is reported to modulate the X‐ray detection efficacy of organic–inorganic hybrids. Deliberately blending the stimuli‐responsive terpyridine carboxylate ligand (tpc−) and the auxiliary pba− group with different ratios gives rise to two OD thorium‐bearing clusters (Th‐102 and Th‐103) and a 1D coordination polymer (Th‐104). Notably, distinct X‐ray sensitivity is evident as a function of molar ratio of the tpc− ligand, following the trend of Th‐102 > Th‐103 > Th‐104. Moreover, Th‐102, which is exclusively built from the tpc− ligands with the highest degree of π–π interactions, exhibits the most sensitive radiochromic and fluorochromic responses toward X‐ray with the lowest detection limit of 1.5 mGy. The study anticipates that this mixed‐ligand strategy will be a versatile approach to tune the X‐ray sensing efficacy of organic–inorganic hybrids

Effect of Return Fines Embedding on the Sintering Behaviour of Vanadium Titanium Magnetite Concentrates

To improve the permeability of sinter packed bed for achieving the efficient utilization of low-grade iron bearing minerals, the effect of the returned fines embedding on productivity, yield, flame front speed (FFS) in the vanadium titanium magnetite (VTM) sintering process, tumble index (TI) of sinter, and permeability of the sinter packed bed was clarified. Results indicate that the productivity, yield, flame front speed, and tumble index of the vanadium titanium magnetite sintering process are all increased to a certain extent after embedding different sizes of returned fines, and the optimal sintering indices occur when the particle size of return fines for embedding is 3~5 mm. The optimal mass ratio of return fines for embedding was confirmed at 80%, and a continued increase in the mass ratio results in a decrease in flame front speed, yield, productivity, and tumble strength. Among the five different possible locations of embedded return fine layer, the middle-lower layer corresponds to the highest flame front speed. As the mass ratio of return fines for embedding is enhanced from 0% to 50%, the permeability of the sinter packed bed is improved at each stage of sintering

Two-Dimensional Inorganic Cationic Network of Thorium Iodate Chloride with Unique Halogen–Halogen Bonds

A unique two-dimensional inorganic cationic network with the formula [Th₃O₂(IO₃)₅(OH)₂]Cl was synthesized hydrothermally. Its crystal structure can best be described as positively charged slabs built with hexanuclear thorium clusters connected by iodate trigonal pyramids. Additional chloride anions are present in the interlayer spaces but surprisingly are not exchangeable, as demonstrated by a series of CrO₄^2– uptake experiments. This is because all chloride anions are trapped by multiple strong halogen–halogen interactions with short Cl–I bond lengths ranging from 3.134 to 3.333 Å, forming a special Cl-centered trigonal-pyramidal polyhedron as a newly observed coordination mode for halogen bonds. Density functional theory calculations clarified that electrons transformed from central Cl atoms to I atoms, generating a halogen–halogen interaction energy with a value of about −8.3 kcal mol^–1 per Cl···I pair as well as providing a total value of −57.9 kcal mol^–1 among delocalized halogen–halogen bonds, which is a new record value reported for a single halogen atom. Additional hydrogen-bonding interaction is also present between Cl and OH, and the interaction energy is predicted to be −8.1 kcal mol^–1, confirming the strong total interaction to lock the interlayer Cl anions