SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models

Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. The majority of such techniques consider solving the diffusion ODE due to its superior efficiency. However, stochastic sampling could offer additional advantages in generating diverse and high-quality data. In this work, we engage in a comprehensive analysis of stochastic sampling from two aspects: variance-controlled diffusion SDE and linear multi-step SDE solver. Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality. Our experiments show that SA-Solver achieves: 1) improved or comparable performance compared with the existing state-of-the-art sampling methods for few-step sampling; 2) SOTA FID scores on substantial benchmark datasets under a suitable number of function evaluations (NFEs)

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connections between the encoder and decoder blocks. Although skip connections have been shown to improve training stability and model performance, we reveal that such shortcuts can be a limiting factor for the complexity of the transformation. As the sampling steps decrease, the generation process and the role of the UNet get closer to the push-forward transformations from Gaussian distribution to the target, posing a challenge for the network's complexity. To address this challenge, we propose Skip-Tuning, a simple yet surprisingly effective training-free tuning method on the skip connections. Our method can achieve 100% FID improvement for pretrained EDM on ImageNet 64 with only 19 NFEs (1.75), breaking the limit of ODE samplers regardless of sampling steps. Surprisingly, the improvement persists when we increase the number of sampling steps and can even surpass the best result from EDM-2 (1.58) with only 39 NFEs (1.57). Comprehensive exploratory experiments are conducted to shed light on the surprising effectiveness. We observe that while Skip-Tuning increases the score-matching losses in the pixel space, the losses in the feature space are reduced, particularly at intermediate noise levels, which coincide with the most effective range accounting for image quality improvement

Stabilization of Discrete-Time Markovian Jump Systems via Controllers with Partially Mode-Dependent Characterization

A kind of stabilizing controller in terms of being partially mode-dependent is developed for discrete-time Markovian jump systems (MJSs). The property referred to be partially mode-dependent is described by the Bernoulli variable. Based on the established model, the stabilization for MJSs over unreliable networks is considered, where both network-induced delay and packet dropout take place in system modes and states. Such effects of network are taken into account in controller design. All the conditions are derived in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are presented to show the effectiveness and applicability of the proposed method

Exosomal lipid PI4P regulates small extracellular vesicle secretion by modulating intraluminal vesicle formation

Abstract Membrane lipids play vital roles in small extracellular vesicle (sEV) biogenesis. However, the function of various lipids in the biogenesis of sEVs is still poorly understood. Phosphoinositolphosphates (PIPs), a group of the most critical lipids in vesicle transport, can undergo rapid conversion in response to a variety of cell signals, which in turn influence the generation of vesicles. Due to the challenge in detecting the low amount of PIP content in biological samples, the function of PIPs in sEVs has been insufficiently investigated. Here, we employed an LC‐MS/MS method to detect the levels of PIPs in sEVs. We revealed phosphatidylinositol‐4‐phosphate (PI4P) was the main PI‐monophosphate in macrophage‐derived sEVs. The release of sEVs was regulated in a time‐dependent manner and correlated with the PI4P level during the lipopolysaccharide (LPS) stimulation. In terms of mechanism, within 10 h of LPS treatment, the LPS‐induced production of type I interferon inhibited the expression of PIP‐5‐kinase‐1‐gamma, which increased the PI4P content on multivesicular bodies (MVBs) and recruited RAB10, member RAS oncogene family, to promote sEV generation. When LPS stimulation was extended to 24 h, the heat shock protein family A member 5 (HSPA5) expression level was elevated. PI4P interacted with HSPA5 on the Golgi or endoplasmic reticulum away from MVBs, which disrupted the continuous fast sEV release. In conclusion, the present study demonstrated an inducible sEV release model response to LPS treatment. The inducible release may be due to PI4P regulating the generation of intraluminal vesicles secreted as sEVs

Designing Catalysts for Chirality-Selective Synthesis of Single-Walled Carbon Nanotubes: Past Success and Future Opportunity

A major obstacle for the applications of single-walled carbon nanotubes (SWNTs) in electronic devices is their structural diversity, ending in SWNTs with diverse electrical properties. Catalytic chemical vapor deposition has shown great promise in directly synthesizing high-quality SWNTs with a high selectivity to specific chirality (n, m). During the growth process, the tube-catalyst interface plays crucial roles in regulating the SWNT nucleation thermodynamics and growth kinetics, ultimately governing the SWNT chirality distribution. Starting with the introduction of SWNT growth modes, this review seeks to extend the knowledge about chirality-selective synthesis by clarifying the energetically favored SWNT cap nucleation and the threshold step for SWNT growth, which describes how the tube-catalyst interface affects both the nucleus energy and the new carbon atom incorporation. Such understandings are subsequently applied to interpret the (n, m) specific growth achieved on a variety of templates, such as SWNT segments or predefined molecular seeds, transition metal (Fe, Co and Ni)-containing catalysts at low reaction temperatures, W-based alloy catalysts, and metal carbides at relatively high reaction temperatures. The up to date achievements on chirality-controlled synthesis of SWNTs is summarized and the remaining major challenges existing in the SWNT synthesis field are discussed.</p