128 research outputs found
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
With the increasing adoption of data-hungry machine learning algorithms,
personal data privacy has emerged as one of the key concerns that could hinder
the success of digital transformation. As such, Privacy-Preserving Machine
Learning (PPML) has received much attention from both academia and industry.
However, organizations are faced with the dilemma that, on the one hand, they
are encouraged to share data to enhance ML performance, but on the other hand,
they could potentially be breaching the relevant data privacy regulations.
Practical PPML typically allows multiple participants to individually train
their ML models, which are then aggregated to construct a global model in a
privacy-preserving manner, e.g., based on multi-party computation or
homomorphic encryption. Nevertheless, in most important applications of
large-scale PPML, e.g., by aggregating clients' gradients to update a global
model for federated learning, such as consumer behavior modeling of mobile
application services, some participants are inevitably resource-constrained
mobile devices, which may drop out of the PPML system due to their mobility
nature. Therefore, the resilience of privacy-preserving aggregation has become
an important problem to be tackled. In this paper, we propose a scalable
privacy-preserving aggregation scheme that can tolerate dropout by participants
at any time, and is secure against both semi-honest and active malicious
adversaries by setting proper system parameters. By replacing
communication-intensive building blocks with a seed homomorphic pseudo-random
generator, and relying on the additive homomorphic property of Shamir secret
sharing scheme, our scheme outperforms state-of-the-art schemes by up to
6.37 in runtime and provides a stronger dropout-resilience. The
simplicity of our scheme makes it attractive both for implementation and for
further improvements.Comment: 16 pages, 5 figures. Accepted by IEEE Transactions on Information
Forensics and Securit
Multifunctional imaging enabled by optical bound states in the continuum with broken symmetry
For photonic crystal slab (PCS) structures, bound states in the continuum
(BICs) and circularly polarized states (dubbed C-points) are important
topological polarization singularities in momentum-space and have attracted
burgeoning attention due to their novel topological and optical properties. In
our work, the evolution of polarization singularities from BICs to C-points is
achieved by breaking the in-plane C2 symmetry of a PCS structure of a square
lattice with C4v symmetry. Correspondingly, a BIC is split into two C-points
with opposite chirality, incurring distinct optical transmission responses with
the incidence of right or left circular polarization (RCP or LCP). Harnessing
such chirality selectivity of the C-points, we propose a multifunctional
imaging system by integrating the designed PCS into a conventional 4-f imaging
system, to realize both the edge imaging and conventional bright-field imaging,
determined by the circular polarization state of the light source. In addition
to multifunctional imaging, our system also provides a vivid picture about the
evolution of the PCS platforms' singularities.Comment: 11 pages, 4 figure
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Recent CLIP-guided 3D optimization methods, such as DreamFields and
PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D
synthesis. However, due to scratch training and random initialization without
prior knowledge, these methods often fail to generate accurate and faithful 3D
structures that conform to the input text. In this paper, we make the first
attempt to introduce explicit 3D shape priors into the CLIP-guided 3D
optimization process. Specifically, we first generate a high-quality 3D shape
from the input text in the text-to-shape stage as a 3D shape prior. We then use
it as the initialization of a neural radiance field and optimize it with the
full prompt. To address the challenging text-to-shape generation task, we
present a simple yet effective approach that directly bridges the text and
image modalities with a powerful text-to-image diffusion model. To narrow the
style domain gap between the images synthesized by the text-to-image diffusion
model and shape renderings used to train the image-to-shape generator, we
further propose to jointly optimize a learnable text prompt and fine-tune the
text-to-image diffusion model for rendering-style image generation. Our method,
Dream3D, is capable of generating imaginative 3D content with superior visual
quality and shape accuracy compared to state-of-the-art methods.Comment: Accepted by CVPR 2023. Project page:
https://bluestyle97.github.io/dream3d
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Surface defect inspection is of great importance for industrial manufacture
and production. Though defect inspection methods based on deep learning have
made significant progress, there are still some challenges for these methods,
such as indistinguishable weak defects and defect-like interference in the
background. To address these issues, we propose a transformer network with
multi-stage CNN (Convolutional Neural Network) feature injection for surface
defect segmentation, which is a UNet-like structure named CINFormer. CINFormer
presents a simple yet effective feature integration mechanism that injects the
multi-level CNN features of the input image into different stages of the
transformer network in the encoder. This can maintain the merit of CNN
capturing detailed features and that of transformer depressing noises in the
background, which facilitates accurate defect detection. In addition, CINFormer
presents a Top-K self-attention module to focus on tokens with more important
information about the defects, so as to further reduce the impact of the
redundant background. Extensive experiments conducted on the surface defect
datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer
achieves state-of-the-art performance in defect detection
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Surface defect inspection is a very challenging task in which surface defects
usually show weak appearances or exist under complex backgrounds. Most
high-accuracy defect detection methods require expensive computation and
storage overhead, making them less practical in some resource-constrained
defect detection applications. Although some lightweight methods have achieved
real-time inference speed with fewer parameters, they show poor detection
accuracy in complex defect scenarios. To this end, we develop a Global Context
Aggregation Network (GCANet) for lightweight saliency detection of surface
defects on the encoder-decoder structure. First, we introduce a novel
transformer encoder on the top layer of the lightweight backbone, which
captures global context information through a novel Depth-wise Self-Attention
(DSA) module. The proposed DSA performs element-wise similarity in channel
dimension while maintaining linear complexity. In addition, we introduce a
novel Channel Reference Attention (CRA) module before each decoder block to
strengthen the representation of multi-level features in the bottom-up path.
The proposed CRA exploits the channel correlation between features at different
layers to adaptively enhance feature representation. The experimental results
on three public defect datasets demonstrate that the proposed network achieves
a better trade-off between accuracy and running efficiency compared with other
17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy
(91.79% , 93.55% , and 97.35% ) on
SD-saliency-900 while running 272fps on a single gpu
Enhanced Spontaneous Antibacterial Activity of delta-MnO2 by Alkali Metals Doping
Recently, the widespread use of antibiotics is becoming a serious worldwide public health challenge, which causes antimicrobial resistance and the occurrence of superbugs. In this context, MnO2 has been proposed as an alternative approach to achieve target antibacterial properties on Streptococcus mutans (S. mutans). This requires a further understanding on how to control and optimize antibacterial properties in these systems. We address this challenge by synthesizing delta-MnO2 nanoflowers doped by magnesium (Mg), sodium (Na), and potassium (K) ions, thus displaying different bandgaps, to evaluate the effect of doping on the bacterial viability of S. mutans. All these samples demonstrated antibacterial activity from the spontaneous generation of reactive oxygen species (ROS) without external illumination, where doped MnO2 can provide free electrons to induce the production of ROS, resulting in the antibacterial activity. Furthermore, it was observed that delta-MnO2 with narrower bandgap displayed a superior ability to inhibit bacteria. The enhancement is mainly attributed to the higher doping levels, which provided more free electrons to generate ROS for antibacterial effects. Moreover, we found that delta-MnO2 was attractive for in vivo applications, because it could nearly be degraded into Mn ions completely following the gradual addition of vitamin C. We believe that our results may provide meaningful insights for the design of inorganic antibacterial nanomaterials.Peer reviewe
Unveiling the pathogenesis and therapeutic approaches for diabetic nephropathy: insights from panvascular diseases
Diabetic nephropathy (DN) represents a significant microvascular complication in diabetes, entailing intricate molecular pathways and mechanisms associated with cardiorenal vascular diseases. Prolonged hyperglycemia induces renal endothelial dysfunction and damage via metabolic abnormalities, inflammation, and oxidative stress, thereby compromising hemodynamics. Concurrently, fibrotic and sclerotic alterations exacerbate glomerular and tubular injuries. At a macro level, reciprocal communication between the renal microvasculature and systemic circulation establishes a pernicious cycle propelling disease progression. The current management approach emphasizes rigorous control of glycemic levels and blood pressure, with renin-angiotensin system blockade conferring renoprotection. Novel antidiabetic agents exhibit renoprotective effects, potentially mediated through endothelial modulation. Nonetheless, emerging therapies present novel avenues for enhancing patient outcomes and alleviating the disease burden. A precision-based approach, coupled with a comprehensive strategy addressing global vascular risk, will be pivotal in mitigating the cardiorenal burden associated with diabetes
Durable superhydrophobic polyvinylidene fluoride membranes via facile spray-coating for effective membrane distillation
Membrane wetting and fouling substantially limits application and deployment of membrane distillation process. Designing high-performance superhydrophobic membranes offers an effective solution to solve the challenge. In this work, a highly durable superhydrophobic surface (water contact angle of 170.8 ± 1.3°) was constructed via a facile and rapid spray-coating of extremely hydrophobic SiO2 nanoparticles onto a porous polyvinylidene fluoride (PVDF) substrate for membrane distillation. The superhydrophobic membrane coated by fluorinated SiO2 nanoparticles exhibited a superior physicochemical stability in a wide range of extreme environments (i.e., NaOH, HCl, hot water, rust water, humic acid solution, ultrasonication, and high-speed water scouring). During 8-h continuous membrane distillation desalination experiment, the coated superhydrophobic membrane experienced a consistently stable water vapor flux (ca. 19.1 kg·m−2·h−1) and desalination efficiency (99.99 %). Additionally, such a stable superhydrophobicity endowed the spray-coated PVDF membrane to overcome membrane wetting and fouling during membrane distillation of highly saline solutions containing foulants (i.e., humic acid and rust). Results reported in this study provides a useful concept and strategy in facile construction of robust superhydrophobic membranes via spray-coating for effective membrane distillation.</p
Privacy-Preserving Aggregation in Federated Learning: A Survey
Over the recent years, with the increasing adoption of Federated Learning
(FL) algorithms and growing concerns over personal data privacy,
Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention
from both academia and industry. Practical PPFL typically allows multiple
participants to individually train their machine learning models, which are
then aggregated to construct a global model in a privacy-preserving manner. As
such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has
received substantial research interest. This survey aims to fill the gap
between a large number of studies on PPFL, where PPAgg is adopted to provide a
privacy guarantee, and the lack of a comprehensive survey on the PPAgg
protocols applied in FL systems. In this survey, we review the PPAgg protocols
proposed to address privacy and security issues in FL systems. The focus is
placed on the construction of PPAgg protocols with an extensive analysis of the
advantages and disadvantages of these selected PPAgg protocols and solutions.
Additionally, we discuss the open-source FL frameworks that support PPAgg.
Finally, we highlight important challenges and future research directions for
applying PPAgg to FL systems and the combination of PPAgg with other
technologies for further security improvement.Comment: 20 pages, 10 figure
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