Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.Comment: Accepted by CVPR2023. Code is available at https://github.com/CGCL-codes/TeC

Secure Single-Server Fuzzy Deduplication without Interactive Proof-of-Ownership in Cloud

The redundant of multimedia data made an unnecessary waste in encrypted cloud storage, unlike text with completely consistent content, multimedia data allows a certain degree of similarity in deduplication, In this work, we focus on the multimedia data which takes a seriously proportion of storage in scenarios such as data outsourcing to propose secure fuzzy deduplication without the additional servers based on Convergent Encryption(CE), say the Single-server Fuzzy Deduplication (SSFD). Compared to the related fuzzy deduplication, SSFD is strong at resisting brute-force attacks caused by server-server collusion, moreover, we also put server-client collusion attacks into security solutions. Additionally, to enhance the security of data, the proposed scheme provides both protection against replay attacks and verification of label consistency and adds no extra communication such as Proof of Ownership(PoW) in interaction. We separately presented a formal security analysis and performed performance at last to prove security solutions and evaluate the experimental results, it shows SSFD provides both a reliable fuzzy images secure deduplication protocol and a computationally feasible solution

Experience Based Quality Control in IMRT Treatment Planning of High Risk Post-Prostatectomy Prostate Cancer with RapidPlan

Purpose: To develop a knowledge based planning (KBP) model with RapidPlan (Varian Medical Systems, Palo Alto, USA) for the treatment of high risk post-prostatectomy prostate cancer. The model was trained on a knowledge database of high quality treatment plans from the national clinical trial RTOG 0621, then tested as a QA tool. Methods: An initial dosimetric analysis was carried out to identify high quality plans from clinical trial RTOG 0621. Treatment plans for patients enrolled in the trial were scored according to the system used by the Imaging and Radiation Oncology Core (IROC) of the National Clinical Trials Network (NCTN) of the NCI to assess adherence to the trial protocol. Of the 80 plans enrolled in the trial 39 were chosen for the training sample. Another subset of 8 plans, orthogonal to the training sample, was chosen for the validation sample to ensure that the model accurately predicts dose volume histograms (DVHs) for all critical structures. The validation plans were then re-optimized with the model in order to test its effectiveness as a tool for planning QA. DVHs of the re-optimized plans were compared with those of the original clinical plans. Normal tissue complication probabilities and tumor control probabilities were calculated with the Lyman-Kutcher-Burman (LKB) model before and after re-optimization to determine the effect on patient outcome. Results: The RapidPlan prostate model was shown to accurately predict estimated DVH bands for all plans in the validation sample that matched the geometry of the training sample. Three treatment plans in the validation sample were geometric outliers with respect to the training sample leading to inaccuracies in the model predictions for the cone down phase of these treatment plans. All of the re-optimized plans showed increased dose sparring to the bladder and rectum respectively without lose of target coverage. The average reduction in NTCP was 0.34 ± 0.21 % for the bladder and 0.11 ± 0.25 % for the rectum with corresponding p-values of 0.116 and 0.668. The average TCP for the prostate bed decreased slightly from 97.05 % to 96.54 % with a p-value of 0.149. Due to limited statistics the changes reported in these numbers are not statistically significant as indicated by the p-values. Although the average values are inconclusive the model was effectively used to identify sub-optimal treatment plans which were improved through re-optimization with the model. For treatment plan 0621c0027 the NTCP decreased from 0.35 % to 0.06 % for the bladder and from 0.10 % to 0.06 % for the rectum while the TCP increased from 96.78 % to 96.87 %. Conclusions: The RapidPlan prostate model developed in this study is an effective tool for monitoring the quality of IMRT treatment plans for high-risk post prostatectomy prostate cancer

Grouting solidification technology for fractured soft coal seams and its application in coalbed methane (coal mine gas) extraction

This article presents an effective method for improving the structure and performance of coal rock masses, thereby facilitating coalbed methane extraction—the grouting solidification technique. A systematic review is conducted on grouting solidification materials, process methods, evaluation techniques, and other related aspects. In conclusion, it is emphasized that the grouting solidification technique requires further refinement in its system, and its continued significance in the dynamically evolving energy and coal industry is underscored. This is crucial for ensuring the efficient development and sustainable utilization of coal and coalbed methane resources

Localized High-Concentration Electrolytes Get More Localized Through Micelle-Like Structures

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media despite having complex chemical composition and atomistic solvation structures, leaving a knowledge gap of microstructural characteristics. Here, we reveal a unique micelle-like structure in a localized high-concentration electrolyte (LHCE), in which the solvent acts as a surfactant between an insoluble salt in diluent. The miscibility of the solvent with the diluent and simultaneous solubility of the salt results in a micelle-like structure with a smeared interface and an increased salt concentration at the centre of the salt-solvent clusters that extends the salt solubility. These intermingling miscibility effects have temperature dependencies, wherein an exemplified LHCE peaks in localized cluster salt concentration near room temperature and is utilized to form a stable solid-electrolyte interphase (SEI) on Li-metal anode. These findings serve as a guide to predicting a stable ternary phase diagram and connecting the electrolyte microstructure with electrolyte formulation and formation protocols to form stable SEI for enhanced battery cyclability

HyperService: Interoperability and Programmability Across Heterogeneous Blockchains

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchange between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchange. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in about 35,000 lines of code to demonstrate its practicality. Our experiment results show that HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applicationsComment: An extended version of the material published in ACM CCS 201

FfDL : A Flexible Multi-tenant Deep Learning Platform

Deep learning (DL) is becoming increasingly popular in several application domains and has made several new application features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. feasible and accurate. As a result, large scale on-premise and cloud-hosted deep learning platforms have become essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM. We describe how our design balances dependability with scalability, elasticity, flexibility and efficiency. We examine FfDL qualitatively through a retrospective look at the lessons learned from building, operating, and supporting FfDL; and quantitatively through a detailed empirical evaluation of FfDL, including the overheads introduced by the platform for various deep learning models, the load and performance observed in a real case study using FfDL within our organization, the frequency of various faults observed including unanticipated faults, and experiments demonstrating the benefits of various scheduling policies. FfDL has been open-sourced.Comment: MIDDLEWARE 201

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies