Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

As data become increasingly vital for deep learning, a company would be very cautious about releasing data, because the competitors could use the released data to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, imperceptible perturbations could be added to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek adversarial examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by modeling checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures in conventional ensemble. That is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small $\ell_\infty=2/255$ perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56\% to 14.68\%, compared to 41.35\% by the best-known method.Code is available at https://github.com/Sizhe-Chen/SEP

Capturing a Metastable Silver(I) Compound of Pyrazine-2,3-dicarboxylic Acid

A metastable 3D supramolecular compound [Ag(NH3)(2)]-[Ag-3(pzdca)(2)(NH3)(4)] (H(2)pzdca = pyrazine-2,3-dicarboxylic acid) (1) has been synthesized and characterized crystallographically. This study offers important information to in-depth understanding of the self-assembly process involving Ag-1 under ammoniacal conditions and conforms that [Ag(NH3)(2)](+)-containing compounds are the intermediates during synthesis of some Ag-1 coordination polymers.National Natural Science Foundation of China [20721001]; MSTC [2007CB815301]; National Science Fund of China for Fostering Talents in Basic Science [J0630429

Fast kNN Graph Construction with Locality Sensitive Hashing

Abstract. The k nearest neighbors (kNN) graph, perhaps the most popular graph in machine learning, plays an essential role for graphbased learning methods. Despite its many elegant properties, the brute force kNN graph construction method has computational complexity of O(n 2 ), which is prohibitive for large scale data sets. In this paper, based on the divide-and-conquer strategy, we propose an efficient algorithm for approximating kNN graphs, which has the time complexity of O(l(d + log n)n) only (d is the dimensionality and l is usually a small number). This is much faster than most existing fast methods. Specifically, we engage the locality sensitive hashing technique to divide items into small subsets with equal size, and then build one kNN graph on each subset using the brute force method. To enhance the approximation quality, we repeat this procedure for several times to generate multiple basic approximate graphs, and combine them to yield a high quality graph. Compared with existing methods, the proposed approach has features that are: (1) much more efficient in speed (2) applicable to generic similarity measures; (3) easy to parallelize. Finally, on three benchmark large-scale data sets, our method beats existing fast methods with obvious advantages

MiR–20a-5p promotes radio-resistance by targeting Rab27B in nasopharyngeal cancer cells

Additional file 3: Figure S2. The protein level of PARP and caspase3 detected by western in NCM, 5PM, NCA, 5PA, si-NC and si-Rab27B transfected CNE-2 and CNE-1 cells respectively

Hemoglobin level is negatively associated with sarcopenia and its components in Chinese aged 60 and above

Introduction: Sarcopenia and low hemoglobin level are common in older adults. Few studies have evaluated the association between hemoglobin level and sarcopenia and with inconsistent findings. The multifaceted effects of sarcopenia on the human body and the high prevalence of anemia in the Chinese population make it necessary to explore the association between the two. Methods: Using the China Health and Retirement Longitudinal Study (CHARLS), we explored the association between hemoglobin with sarcopenia and its components in the Chinese population aged 60 and above. Multivariate logistic and Cox proportional hazards models were constructed to examine the association of hemoglobin level with sarcopenia and sarcopenia components in individuals aged 60 years or above. The subgroup analysis covered residence, body mass index level, drinking status, and smoking status were conducted. The possible difference of associations between sexes was also explored. Results: With a total of 3,055 people, the hemoglobin concentration in people without sarcopenia, possible sarcopenia, and sarcopenia are 14.34 ± 2.22, 14.64 ± 2.27, and 13.58 ± 2.02 g/dl, respectively. Cross-sectional analysis showed strong evidence that hemoglobin was negatively associated with sarcopenia [Odds Ratio (OR) = 0.95, 95% Confidence Interval (CI): 0.90–0.99] and low height-adjusted appendicular skeletal muscle mass (OR = 0.91, 95% CI: 0.86–0.97). On average, a per 1 g/dl higher hemoglobin level was associated with 5% lower odds of sarcopenia (OR = 0.95, 95% CI: 0.90–0.98). The cohort study of 1,022 people demonstrated a statistically significant negative association of hemoglobin level with low physical performance [Hazard Ratio (HR) = 0.92, 95% CI: 0.85–0.99], merely with sarcopenia (HR = 0.92, 95% CI: 0.84–1.00) and skeletal muscle mass (HR = 0.95, 95% CI: 0.80–1.00). Sex-specific analysis suggested hemoglobin's association with sarcopenia, muscle mass, and physical performance in all sexes, with weaker magnitudes in females. Hemoglobin in urban residents and people with high body mass index (BMI) has a larger magnitude of the negative association with sarcopenia. Discussion: Hemoglobin level associates with sarcopenia, muscle mass, and physical performance in the Chinese population aged 60 and above, with sex-specific, residence-specific, and BMI-specific effects

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

ABSTRACTUrban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects