24 research outputs found
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Two-party computation (2PC) is promising to enable privacy-preserving deep
learning (DL). However, the 2PC-based privacy-preserving DL implementation
comes with high comparison protocol overhead from the non-linear operators.
This work presents PASNet, a novel systematic framework that enables low
latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by
integrating the hardware latency of the cryptographic building block into the
neural architecture search loss function. We develop a cryptographic hardware
scheduler and the corresponding performance model for Field Programmable Gate
Arrays (FPGA) as a case study. The experimental results demonstrate that our
light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms
and 228 ms latency on private inference on ImageNet, which are 147 and 40 times
faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and
more than 1000 times higher energy efficiency.Comment: DAC 2023 accepeted publication, short version was published on AAAI
2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards
ReLU-Reduced Neural Network for Two-party Computation Based Private Inferenc
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging
privacy and security concerns. To mitigate these issues, secure multi-party
computation (MPC) has been discussed, to enable the privacy-preserving DL
computation. In practice, they often come at very high computation and
communication overhead, and potentially prohibit their popularity in large
scale systems. Two orthogonal research trends have attracted enormous interests
in addressing the energy efficiency in secure deep learning, i.e., overhead
reduction of MPC comparison protocol, and hardware acceleration. However, they
either achieve a low reduction ratio and suffer from high latency due to
limited computation and communication saving, or are power-hungry as existing
works mainly focus on general computing platforms such as CPUs and GPUs.
In this work, as the first attempt, we develop a systematic framework,
PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware
acceleration, by integrating hardware latency of the cryptographic building
block into the DNN loss function to achieve high energy efficiency, accuracy,
and security guarantee. Instead of heuristically checking the model sensitivity
after a DNN is well-trained (through deleting or dropping some non-polynomial
operators), our key design principle is to em enforce exactly what is assumed
in the DNN design -- training a DNN that is both hardware efficient and secure,
while escaping the local minima and saddle points and maintaining high
accuracy. More specifically, we propose a straight through polynomial
activation initialization method for cryptographic hardware friendly trainable
polynomial activation function to replace the expensive 2P-ReLU operator. We
develop a cryptographic hardware scheduler and the corresponding performance
model for Field Programmable Gate Arrays (FPGA) platform
Beauvericin counteracted multi-drug resistant Candida albicans by blocking ABC transporters
AbstractMulti-drug resistance of pathogenic microorganisms is becoming a serious threat, particularly to immunocompromised populations. The high mortality of systematic fungal infections necessitates novel antifungal drugs and therapies. Unfortunately, with traditional drug discovery approaches, only echinocandins was approved by FDA as a new class of antifungals in the past two decades. Drug efflux is one of the major contributors to multi-drug resistance, the modulator of drug efflux pumps is considered as one of the keys to conquer multi-drug resistance. In this study, we combined structure-based virtual screening and whole-cell based mechanism study, identified a natural product, beauvericin (BEA) as a drug efflux pump modulator, which can reverse the multi-drug resistant phenotype of Candida albicans by specifically blocking the ATP-binding cassette (ABC) transporters; meantime, BEA alone has fungicidal activity in vitro by elevating intracellular calcium and reactive oxygen species (ROS). It was further demonstrated by histopathological study that BEA synergizes with a sub-therapeutic dose of ketoconazole (KTC) and could cure the murine model of disseminated candidiasis. Toxicity evaluation of BEA, including acute toxicity test, Ames test, and hERG (human ether-à-go-go-related gene) test promised that BEA can be harnessed for treatment of candidiasis, especially the candidiasis caused by ABC overexpressed multi-drug resistant C. albicans
Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
PURPOSE:Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs-at-risk (OARs) on H&N CT images is thus essential to treatment quality. Manual contouring used in current clinical practice is tedious, time-consuming, and can produce inconsistent results. Existing automated segmentation methods are challenged by the substantial inter-patient anatomical variation and low CT soft tissue contrast. To overcome the challenges, we developed a novel automated H&N OARs segmentation method that combines a fully convolutional neural network (FCNN) with a shape representation model (SRM). METHODS:Based on manually segmented H&N CT, the SRM and FCNN were trained in two steps: (a) SRM learned the latent shape representation of H&N OARs from the training dataset; (b) the pre-trained SRM with fixed parameters were used to constrain the FCNN training. The combined segmentation network was then used to delineate nine OARs including the brainstem, optic chiasm, mandible, optical nerves, parotids, and submandibular glands on unseen H&N CT images. Twenty-two and 10 H&N CT scans provided by the Public Domain Database for Computational Anatomy (PDDCA) were utilized for training and validation, respectively. Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95%SD) were calculated to quantitatively evaluate the segmentation accuracy of the proposed method. The proposed method was compared with an active appearance model that won the 2015 MICCAI H&N Segmentation Grand Challenge based on the same dataset, an atlas method and a deep learning method based on different patient datasets. RESULTS:An average DSC = 0.870 (brainstem), DSC = 0.583 (optic chiasm), DSC = 0.937 (mandible), DSC = 0.653 (left optic nerve), DSC = 0.689 (right optic nerve), DSC = 0.835 (left parotid), DSC = 0.832 (right parotid), DSC = 0.755 (left submandibular), and DSC = 0.813 (right submandibular) were achieved. The segmentation results are consistently superior to the results of atlas and statistical shape based methods as well as a patch-wise convolutional neural network method. Once the networks are trained off-line, the average time to segment all 9 OARs for an unseen CT scan is 9.5 s. CONCLUSION:Experiments on clinical datasets of H&N patients demonstrated the effectiveness of the proposed deep neural network segmentation method for multi-organ segmentation on volumetric CT scans. The accuracy and robustness of the segmentation were further increased by incorporating shape priors using SMR. The proposed method showed competitive performance and took shorter time to segment multiple organs in comparison to state of the art methods
Acoustic topological phase transition induced by band inversion of high-order compound modes and robust pseudospin-dependent transport
Using Neural Networks to Extend Cropped Medical Images for Deformable Registration Among Images with Differing Scan Extents
PURPOSE: Missing or discrepant imaging volumes is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT’s and then test its use in DIR. METHODS: Using a training dataset of 409 head and neck CT’s, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. The network used a 3D U-Net generator along with VGG deep feature losses. To test our technique, for each of the 53 test volumes, we used Elastix to deformably register combinations of a randomly cropped, full, and synthetically full volume to a single cropped, full, and synthetically full target volume. We additionally tested our method’s robustness to crop extent by progressively increasing the amount of cropping, synthesizing the missing anatomy using our network, then performing the same registration combinations. Registration performance was measured using 95% Hausdorff distance across 16 contours. RESULTS: We successfully trained a network to synthesize missing anatomy in superiorly and inferiorly cropped images. The network can estimate large regions in an incomplete image, far from the cropping boundary. Registration using our estimated full images was not significantly different from registration using the original full images. The average contour matching error for full image registration was 9.9mm, while our method was 11.6mm, 12.1mm, and 13.6mm for synthesized-to-full, full-to-synthesized, and synthesized-to-synthesized registrations, respectively. In comparison, registration using the cropped images had errors of 31.7mm and higher. Plotting the registered image contour error as a function of initial pre-registered error shows that our method is robust to registration difficulty. Synthesized-to-full registration was statistically independent of cropping extent up to 18.7cm superiorly cropped. Synthesized-to-synthesized registration was nearly independent, with a −0.04mm change in average contour error for every additional millimeter of cropping. CONCLUSIONS: Different or inadequate in scan extent is a major cause of DIR inaccuracies. We address this challenge by training a neural network to complete cropped 3D images. We show that with image completion, the source of DIR inaccuracy is eliminated, and the method is robust to varying crop extent
Coumarins from Edgeworthia chrysantha
A new coumarin, edgeworic acid (1), was isolated from the flower buds of Edgeworthia chrysantha, together with the five known coumarins umbelliferone (2), 5,7-dimethoxycoumarin (3), daphnoretin (4), edgeworoside C (5), and edgeworoside A (6). Their structures were established on the basis of spectral data, particularly by the use of 1D NMR and several 2D shift-correlated NMR pulse sequences (1H-1H COSY, HSQC and HMBC), in combination with acetylation reactions