Application of deep neural network to the reconstruction of two-phase material imaging by capacitively coupled electrical resistance tomography

A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided

PDS-MAR: a fine-grained Projection-Domain Segmentation-based Metal Artifact Reduction method for intraoperative CBCT images with guidewires

Since the invention of modern CT systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and insufficient data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. In this paper, we demonstrate that conventional image-domain segmentation-based MAR methods are unable to eliminate metal artifacts for intraoperative CBCT images with guidewires. To solve this problem, we present a fine-grained projection-domain segmentation-based MAR method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. The digital phantom study and real CBCT data study demonstrate that the proposed algorithm achieves significantly better artifact suppression than other comparing methods and has the potential to advance the use of intraoperative CBCT imaging in clinical spine surgeries.Comment: 19 Page

Model Inversion Attack via Dynamic Memory Learning

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts. To address this problem, we propose a novel WavAugment Guided Phoneme Adversarial Training (wapat). wapat use adversarial examples in phoneme space as augmentation to make the model invariant to minor fluctuations in phoneme representation and preserve the performance on clean samples. In addition, wapat utilizes the phoneme representation of augmented samples to guide the generation of adversaries, which helps to find more stable and diverse gradient-directions, resulting in improved generalization. Extensive experiments demonstrate the effectiveness of wapat on End-to-end Speech Challenge Benchmark (ESB). Notably, SpeechLM-wapat outperforms the original model by 6.28% WER reduction on ESB, achieving the new state-of-the-art

TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations

In a transfer-based attack against Automatic Speech Recognition (ASR) systems, attacks are unable to access the architecture and parameters of the target model. Existing attack methods are mostly investigated in voice assistant scenarios with restricted voice commands, prohibiting their applicability to more general ASR related applications. To tackle this challenge, we propose a novel contextualized attack with deletion, insertion, and substitution adversarial behaviors, namely TransAudio, which achieves arbitrary word-level attacks based on the proposed two-stage framework. To strengthen the attack transferability, we further introduce an audio score-matching optimization strategy to regularize the training process, which mitigates adversarial example over-fitting to the surrogate model. Extensive experiments and analysis demonstrate the effectiveness of TransAudio against open-source ASR models and commercial APIs

Enhance the Visual Representation via Discrete Adversarial Training

Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thus has limited usefulness on industrial-scale production and applications. Surprisingly, this phenomenon is totally opposite in Natural Language Processing (NLP) task, where AT can even benefit for generalization. We notice the merit of AT in NLP tasks could derive from the discrete and symbolic input space. For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT). DAT leverages VQGAN to reform the image data to discrete text-like inputs, i.e. visual words. Then it minimizes the maximal risk on such discrete images with symbolic adversarial perturbations. We further give an explanation from the perspective of distribution to demonstrate the effectiveness of DAT. As a plug-and-play technique for enhancing the visual representation, DAT achieves significant improvement on multiple tasks including image classification, object detection and self-supervised learning. Especially, the model pre-trained with Masked Auto-Encoding (MAE) and fine-tuned by our DAT without extra data can get 31.40 mCE on ImageNet-C and 32.77% top-1 accuracy on Stylized-ImageNet, building the new state-of-the-art. The code will be available at https://github.com/alibaba/easyrobust.Comment: Accepted to NeurIPS 2022, https://github.com/alibaba/easyrobus

Identification of Autophagy-Related Gene 7 and Autophagic Cell Death in the Planarian Dugesia japonica

Planarians undergo continuous body size remodeling under starvation or during regeneration. This process likely involves autophagy and autophagic cell death, but this hypothesis is supported by few studies. To test this hypothesis, we cloned and characterized autophagy-related gene 7 (Atg7) from the planarian Dugesia japonica (DjAtg7). The full-length cDNA of DjAtg7 measures 2272 bp and includes a 2082-bp open reading frame encoding 693 amino acids with a molecular weight of 79.06 kDa. The deduced amino acid sequence of DjAtg7 contains a conserved ATP-binding site and a catalytic active site of an E1-like enzyme belonging to the ATG7 superfamily. DjAtg7 transcripts are mainly expressed in intestinal tissues of the intact animals. After amputation, DjAtg7 was highly expressed at the newly regenerated intestinal branch on days 3–7 of regeneration and in the old tissue of the distal intestinal branch on day 10 of regeneration. However, knockdown of DjAtg7 by RNAi did not affect planarian regeneration and did not block autophagosome formation, which indicates that autophagy is more complex than previously expected. Interestingly, TEM clearly confirmed that autophagy and autophagic cell death occurred in the old tissues of the newly regenerated planarians and clearly revealed that the dying cell released vesicles containing cellular cytoplasmic contents into the extracellular space. Therefore, the autophagy and autophagic cell death that occurred in the old tissue not only met the demand for body remodeling but also met the demand for energy supply during planarian regeneration. Collectively, our work contributes to the understanding of autophagy and autophagic cell death in planarian regeneration and body remodeling

Development and validation of a nomogram to predict the five-year risk of revascularization for non-culprit lesion progression in STEMI patients after primary PCI

BackgroundAcute ST-segment elevation myocardial infarction (STEMI) patients after primary PCI were readmitted for revascularization due to non-culprit lesion (NCL) progression.ObjectiveTo develop and validate a nomogram that can accurately predict the likelihood of NCL progression revascularization in STEMI patients following primary PCI.MethodsThe study enrolled 1,612 STEMI patients after primary PCI in our hospital from June 2009 to June 2018. Patients were randomly divided into training and validation sets in a 7:3 ratio. The independent risk factors were determined by LASSO regression and multivariable logistic regression analysis. Multivariate logistic regression analysis was utilized to develop a nomogram, which was then evaluated for its performance using the concordance statistics, calibration plots, and decision curve analysis (DCA).ResultsThe nomogram was composed of five predictors, including age (OR: 1.007 95% CI: 1.005–1.009, P < 0.001), body mass index (OR: 1.476, 95% CI: 1.363–1.600, P < 0.001), triglyceride and glucose index (OR: 1.050, 95% CI: 1.022–1.079, P < 0.001), Killip classification (OR: 1.594, 95% CI: 1.140–2.229, P = 0.006), and serum creatinine (OR: 1.007, 95% CI: 1.005–1.009, P < 0.001). Both the training and validation groups accurately predicted the occurrence of NCL progression revascularization (The area under the receiver operating characteristic curve values, 0.901 and 0.857). The calibration plots indicated an excellent agreement between prediction and observation in both sets. Furthermore, the DCA demonstrated that the model exhibited clinical efficacy.ConclusionA convenient and accurate nomogram was developed and validated for predicting the occurrence of NCL progression revascularization in STEMI patients after primary PCI