Masked Diffusion Models Are Fast and Privacy-Aware Learners

Diffusion models have emerged as the \emph{de-facto} technique for image generation, yet they entail significant computational overhead, hindering the technique's broader application in the research community. We propose a prior-based denoising training framework, the first to incorporate the pre-train and fine-tune paradigm into the diffusion model training process, which substantially improves training efficiency and shows potential in facilitating various downstream tasks. Our approach centers on masking a high proportion (e.g., up to 90\%) of the input image and employing masked denoising score matching to denoise the visible areas, thereby guiding the diffusion model to learn more salient features from training data as prior knowledge. By utilizing masked learning in a pre-training stage, we efficiently train the ViT-based diffusion model on CelebA-HQ $256 \times 256$ in the pixel space, achieving a 4x acceleration and enhancing the quality of generated images compared to denoising diffusion probabilistic model (DDPM). Moreover, our masked pre-training technique can be universally applied to various diffusion models that directly generate images in the pixel space, aiding in the learning of pre-trained models with superior generalizability. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement through fine-tuning with merely 10\% data from a different distribution. Moreover, our method shows the potential to serve as a training paradigm for enhancing the privacy protection capabilities of diffusion models. Our code is available at \url{https://github.com/jiachenlei/maskdm}

Preparation and Mechanical Properties of Continuous Carbon Nanotube Networks Modified C f

Continuous carbon nanotube (CNT) networks were formed in Cf/SiC composites via freeze-drying method. Composites were fabricated by precursor infiltration and pyrolysis (PIP) process afterwards. The different distribution morphologies of CNTs in the preforms originating from the different CNT contents were analyzed while the influence of the distribution of CNTs was discussed in detail. Compared to composites without CNTs, the interfacial shear strength (ILSS) and the flexural strength of Cf/1%CNTs/SiC were increased by 31% and 27%, respectively, but the values of Cf/2.5%CNTs/SiC decreased as a result of lots of defects caused by excess CNTs. With the analysis of ILSS, the flexural strengths, and the fracture morphologies, CNTs effectively improved the weak interfacial strength between T700SC carbon fibers and SiC matrix

Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022

In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made available.Comment: 5 page

60 years development and prospect of mining systems engineering

The mining industry is a sector aimed at the safe, efficient, green development, and clean, efficient, low-carbon utilization of mineral resources, serving as a foundational industry for economic and social development. In order to make mining development more economical, consider a more comprehensive range of factors, and optimize the design and development processes, in the late 1950s, operations research and computer technology were introduced into mining engineering, giving rise to a new discipline known as mining systems engineering. In its early stages, mining systems engineering primarily focused on the optimization of mining production processes and the use of computers in mine design, excavation planning, information processing, and application software development, with a predominant emphasis on open-pit mining as the research object. In recent years, research in mining systems engineering has closely integrated with big data, artificial intelligence, network technology, and has achieved fruitful results in areas such as mining information network construction, logistics systems, intelligent mining, equipment management, and safety management. This paper provides a detailed exposition of the origin and development process of mining systems engineering, with a chronological narrative based on international conferences on the application of operations research and computers in mining (APCOM), particularly the fifteen national conferences on mining systems engineering that have taken place. It objectively summarizes the developmental process and evolution of research content in mining systems engineering. Using the CNKI database as the data foundation, current research employs cluster analysis to study the publication volume, research objects, and their changes in mining systems engineering over the past 20 years. The future development of mining systems engineering should focus on the integration of systems science thinking with modern mining technology, establishing scientific systems and their boundaries, such as scientific mining systems, mine lifecycle systems, intelligent mine systems, and other eight major systems. The development of mining systems engineering will increasingly emphasize innovation and application in aspects such as digitization, intelligence, sustainable development, informatization, and automation, while continually expanding its application areas to achieve optimized, efficient, green, and low-carbon mining development

SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution

Advanced text-to-image models such as DALL-E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult, violent, or deceptive imagery of political figures. Despite claims of rigorous safety mechanisms implemented in these models to restrict the generation of not-safe-for-work (NSFW) content, we successfully devise and exhibit the first prompt attacks on Midjourney, resulting in the production of abundant photorealistic NSFW images. We reveal the fundamental principles of such prompt attacks and suggest strategically substituting high-risk sections within a suspect prompt to evade closed-source safety measures. Our novel framework, SurrogatePrompt, systematically generates attack prompts, utilizing large language models, image-to-text, and image-to-image modules to automate attack prompt creation at scale. Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios. Both subjective and objective assessments validate that the images generated from our attack prompts present considerable safety hazards.Comment: 14 pages, 11 figure

Evidence of spin density waves in La3_3Ni2_2O7−δ_{7-\delta}

The recently discovered superconductivity with critical temperature $T_{c}$ up to 80 K in the Ruddlesden-Popper phases La$_3$Ni$_2$O$_{7-\delta}$ under pressure has drawn great attention. Here we report the positive muon spin relaxation ($\mu^+$SR) study of polycrystalline La$_3$Ni$_2$O$_{7-\delta}$ under ambient pressure. The zero-field $\mu^+$SR experiments reveal the existence of static long range magnetic order in La$_3$Ni$_2$O$_{7-\delta}$, and the the muon spin depolarization spectra are consistent with the spin density wave internal field distribution. The weak transverse field $\mu^+$SR measurements suggest the bulk magnetic transition near $T_{\rm{N}}=148$ K. This is the first research which discovers the existence of the spin density wave in La$_3$Ni$_2$O$_{7-\delta}$ microscopically

miR-30e-5p regulates leukemia stem cell self-renewal through the Cyb561/ROS signaling pathway

Leukemia stem cells (LSC) represent a crucial and rare subset of cells present in acute myeloid leukemia (AML); they play a pivotal role in the initiation, maintenance, and relapse of this disease. Targeting LSC holds great promise for preventing AML relapse and improving long-term outcomes. However the precise molecular mechanisms governing LSC self-renewal are still poorly understood. Here, we present compelling evidence that the expression of miR-30e-5p, a potential tumor-suppressive microRNA, is significantly lower in AML samples than in healthy bone marrow samples. Forced expression of miR- 30e effectively inhibits leukemogenesis, impairs LSC self-renewal, and delays leukemia progression. Mechanistically, Cyb561 acts as a direct target of miR-30e-5p in LSC, and its deficiency restricts the self-renewal of LSC by activating reactive oxygen series signaling and markedly prolongs recipients’ survival. Moreover, genetic or pharmacological overexpression of miR-30e-5p or knockdown of Cyb561 suppresses the growth of human AML cells. In conclusion, our findings establish the crucial role of the miR-30e-5p/Cyb561/ROS axis in finely regulating LSC self-renewal, highlighting Cyb561 as a potential therapeutic target for LSC-directed therapies

Observation of many-body Fock space dynamics in two dimensions

Quantum many-body simulation provides a straightforward way to understand fundamental physics and connect with quantum information applications. However, suffering from exponentially growing Hilbert space size, characterization in terms of few-body probes in real space is often insufficient to tackle challenging problems such as quantum critical behavior and many-body localization (MBL) in higher dimensions. Here, we experimentally employ a new paradigm on a superconducting quantum processor, exploring such elusive questions from a Fock space view: mapping the many-body system onto an unconventional Anderson model on a complex Fock space network of many-body states. By observing the wave packet propagating in Fock space and the emergence of a statistical ergodic ensemble, we reveal a fresh picture for characterizing representative many-body dynamics: thermalization, localization, and scarring. In addition, we observe a quantum critical regime of anomalously enhanced wave packet width and deduce a critical point from the maximum wave packet fluctuations, which lend support for the two-dimensional MBL transition in finite-sized systems. Our work unveils a new perspective of exploring many-body physics in Fock space, demonstrating its practical applications on contentious MBL aspects such as criticality and dimensionality. Moreover, the entire protocol is universal and scalable, paving the way to finally solve a broader range of controversial many-body problems on future larger quantum devices.Comment: 8 pages, 4 figures + supplementary informatio

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.Comment: 10 pages with 2 pages for reference

One-Step Hydrothermal/Solvothermal Preparation of Pt/TiO₂: An Efficient Catalyst for Biobutanol Oxidation at Room Temperature

The selective oxidation of biobutanol to prepare butyric acid is an important conversion process, but the preparation of low-temperature and efficient catalysts for butanol oxidation is currently a bottleneck problem. In this work, we prepared Pt-TiO2 catalysts with different Pt particle sizes using a simple one-step hydrothermal/solvothermal method. Transmission electron microscopy and X-ray diffraction results showed that the average size of the Pt particles ranged from 1.1 nm to 8.7 nm. Among them, Pt-TiO2 with an average particle size of 3.6 nm exhibited the best catalytic performance for biobutanol. It was capable of almost completely converting butanol, even at room temperature (30 °C), with a 98.9% biobutanol conversion, 98.4% butyric acid selectivity, and a turnover frequency (TOF) of 36 h−1. Increasing the reaction temperature to 80 and 90 °C, the corresponding TOFs increased rapidly to 355 and 619 h−1. The relationship between the electronic structure of Pt and its oxidative performance suggests that the synergistic effect of the dual sites, Pt0 and Pt2+, could be the primary factor contributing to its elevated reactivity