80 research outputs found
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 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}
Generalized Neural Collapse for a Large Number of Classes
Neural collapse provides an elegant mathematical characterization of learned
last layer representations (a.k.a. features) and classifier weights in deep
classification models. Such results not only provide insights but also motivate
new techniques for improving practical deep models. However, most of the
existing empirical and theoretical studies in neural collapse focus on the case
that the number of classes is small relative to the dimension of the feature
space. This paper extends neural collapse to cases where the number of classes
are much larger than the dimension of feature space, which broadly occur for
language models, retrieval systems, and face recognition applications. We show
that the features and classifier exhibit a generalized neural collapse
phenomenon, where the minimum one-vs-rest margins is maximized.We provide
empirical study to verify the occurrence of generalized neural collapse in
practical deep neural networks. Moreover, we provide theoretical study to show
that the generalized neural collapse provably occurs under unconstrained
feature model with spherical constraint, under certain technical conditions on
feature dimension and number of classes.Comment: 32 pages, 12 figure
Effect of Ga on the Inoxidizability and Wettability of Sn-0.5Ag-0.7Cu-0.05Pr Solder
The effect of trace amount of Ga on the inoxidizability and wettability of Sn-0.5Ag-0.7Cu-0.05Pr solders was investigated systematically by means of microstructure characterizations. The results indicate that the wettability and oxidation resistance properties are remarkably improved with addition of trace amount of Ga. Moreover, it is observed that the trace amount of Ga in Sn-0.5Ag-0.7Cu-0.05Pr solders refines the matrix microstructure. The relationship between wettability and oxidation resistance was put into deep study. And Ga was found to be enriched on the surface of the molten solder, which benefited the properties correspondingly. The results of this study can stimulate the use of low-silver Sn-Ag-Cu-Pr solders for various applications
CAME: Contrastive Automated Model Evaluation
The Automated Model Evaluation (AutoEval) framework entertains the
possibility of evaluating a trained machine learning model without resorting to
a labeled testing set. Despite the promise and some decent results, the
existing AutoEval methods heavily rely on computing distribution shifts between
the unlabelled testing set and the training set. We believe this reliance on
the training set becomes another obstacle in shipping this technology to
real-world ML development. In this work, we propose Contrastive Automatic Model
Evaluation (CAME), a novel AutoEval framework that is rid of involving training
set in the loop. The core idea of CAME bases on a theoretical analysis which
bonds the model performance with a contrastive loss. Further, with extensive
empirical validation, we manage to set up a predictable relationship between
the two, simply by deducing on the unlabeled/unseen testing set. The resulting
framework CAME establishes a new SOTA results for AutoEval by surpassing prior
work significantly.Comment: ICCV2023 main conferenc
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
Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets
Magnetic structure plays a pivotal role in the functionality of
antiferromagnets (AFMs), which not only can be employed to encode digital data
but also yields novel phenomena. Despite its growing significance, visualizing
the antiferromagnetic domain structure remains a challenge, particularly for
non-collinear AFMs. Currently, the observation of magnetic domains in
non-collinear antiferromagnetic materials is feasible only in MnSn,
underscoring the limitations of existing techniques that necessitate distinct
methods for in-plane and out-of-plane magnetic domain imaging. In this study,
we present a versatile method for imaging the antiferromagnetic domain
structure in a series of non-collinear antiferromagnetic materials by utilizing
the anomalous Ettingshausen effect (AEE), which resolves both the magnetic
octupole moments parallel and perpendicular to the sample surface. Temperature
modulation due to the AEE originating from different magnetic domains is
measured by the lock-in thermography, revealing distinct behaviors of octupole
domains in different antiferromagnets. This work delivers an efficient
technique for the visualization of magnetic domains in non-collinear AFMs,
which enables comprehensive study of the magnetization process at the
microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres
Rechargeable Li/Cl battery down to -80 {\deg}C
Low temperature rechargeable batteries are important to life in cold
climates, polar/deep-sea expeditions and space explorations. Here, we report ~
3.5 - 4 V rechargeable lithium/chlorine (Li/Cl2) batteries operating down to
-80 {\deg}C, employing Li metal negative electrode, a novel CO2 activated
porous carbon (KJCO2) as the positive electrode, and a high ionic conductivity
(~ 5 to 20 mS cm-1 from -80 {\deg}C to 25 {\deg}C) electrolyte comprised of 1 M
aluminum chloride (AlCl3), 0.95 M lithium chloride (LiCl), and 0.05 M lithium
bis(fluorosulfonyl)imide (LiFSI) in low melting point (-104.5 {\deg}C) thionyl
chloride (SOCl2). Between room-temperature and -80 {\deg}C, the Li/Cl2 battery
delivered up to ~ 30,000 - 4,500 mAh g-1 first discharge capacity and a 1,200 -
5,000 mAh g-1 reversible capacity (discharge voltages in ~ 3.5 to 3.1 V) over
up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron
spectroscopy (XPS) probed Cl2 trapped in the porous carbon upon LiCl
electro-oxidation during charging. At lower temperature down to -80 {\deg}C,
SCl2/S2Cl2 and Cl2 generated by electro-oxidation in the charging step were
trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a
high discharge voltage plateau near ~ 4 V with up to ~ 1000 mAh g-1 capacity
for SCl2/S2Cl2 reduction and up to ~ 4000 mAh g-1 capacity at ~ 3.1 V plateau
for Cl2 reduction. Towards practical use, we made CR2032 Li/Cl2 battery cells
to drive digital watches at -40 {\deg}C and light emitting diode at -80
{\deg}C, opening Li/Cl2 secondary batteries for ultra-cold conditions
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
Study on the Characteristics of Surrounding Rock and Design of Backfill Material Parameters for Tunnels Passing through Giant Caverns and Underground Rivers
The influence on tunnel construction of karst and underground rivers is always an important problem in tunnel engineering. In order to demonstrate the rationality of backfill parameters and the effectiveness of supports under the influence of groundwater when a tunnel passes through a large karst cave, the finite element software FLAC3D was used for numerical analysis. Converting the mean values and standard deviations of mechanical response results of the surrounding rock and the supports on multiple sections into the ECULID distance from the origin point on a two-dimensional plane as evaluation indexes, the influence of the reinforcement parameters on the mechanical response of the surrounding rock and the supports was analyzed by orthogonal experiments. Based on fuzzy decision theory, by regarding the ECULID distance between the simulated result of each group and the global optimal value of the multiple evaluation index as the comprehensive evaluation index, a backfill parameter design method was proposed. By comparing the results which used optimal and worst parameters in the FLAC3D, 10 times and 2.5 times differences in longitudinal and horizontal displacement were observed, respectively. Then, the optimal backfill parameters were applied to the actual project for verification. The field monitoring results showed that the optimal backfill parameters can effectively reduce the displacement around the tunnel. After constructing a diversion for the underground river, the water flow in the karst cave did not rise during a rainstorm. This study provides a reference for the design and construction of other projects in the future
Scenarios for a post-COVID-19 world airline network
12 pages, 7 main figures (+5 figures in appendix)The airline industry was severely hit by the COVID-19 crisis with an average demand decrease of about 64%(IATA, April 2020) which triggered already several bankruptcies of airline companies all over the world. While the robustness of the world airline network (WAN) was mostly studied as an homogeneous network, we introduce a new tool for analyzing the impact of a company failure: the `airline company network' where two airlines are connected if they share at least one route segment. Using this tool, we observe that the failure of companies well connected with others has the largest impact on the connectivity of the WAN. We then explore how the global demand reduction affects airlines differently, and provide an analysis of different scenarios if its stays low and does not come back to its pre-crisis level. Using traffic data from the Official Aviation Guide (OAG) and simple assumptions about customer's airline choice strategies, we find that the local effective demand can be much lower than the average one, especially for companies that are not monopolistic and share their segments with larger companies. Even if the average demand comes back to 60% of the total capacity, we find that between 46% and 59% of the companies could experience a reduction of more than 50% of their traffic, depending on the type of competitive advantage that drives customer's airline choice. These results highlight how the complex competitive structure of the WAN weakens its robustness when facing such a large crisis
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