Supplementary Material.docx

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Unraveling the Li₂S Deposition Process on a Polished Graphite Cathode for Enhancing Discharge Capacity of Lithium–Sulfur Batteries

Solid deposition accounts for three-quarters of the theoretical capacity in lithium–sulfur (Li–S) batteries with liquid electrolyte. Despite extensive research efforts on cathode material synthesis, little knowledge has been gained so far in understanding and controlling the growth of solid discharge product in Li–S batteries. In this work, a polished graphite was used as a cathode to understand the growth mechanism of Li2S. The SEM/EDS analysis of the discharged cathodes indicates that the Li2S precipitate can grow over a micrometer in size and its morphology strongly depends on the depth of discharge (DODs) and discharge rate of the cell. In addition, the morphology evolution and the in situ electrochemical impedance spectra (EIS) show that the Li2S follows a dissolution–precipitation mechanism during its deposition on the graphite surface. Finally, a mathematical model based on the multicomponent transport theory is developed and used to describe the nucleation and precipitation phenomena on the 2D surface and the EIS spectra at different DODs. The model confirms that the surface passivation of the cathode plays a major role during the discharge of the battery and offers a simple way to measure experimentally the surface coverage as a function of the DOD in Li–S batteries. This work highlights the importance of deferring cathode surface passivation in Li–S batteries and indicates the potential utilization of nonporous carbons as alternative sulfur hosts

Investigation of Cross-Contamination and Misidentification of 278 Widely Used Tumor Cell Lines

<div><p>In recent years, biological research involving human cell lines has been rapidly developing in China. However, some of the cell lines are not authenticated before use. Therefore, misidentified and/or cross-contaminated cell lines are unfortunately commonplace. In this study, we present a comprehensive investigation of cross-contamination and misidentification for a panel of 278 cell lines from 28 institutes in China by using short tandem repeat profiling method. By comparing the DNA profiles with the cell bank databases of ATCC and DSMZ, a total of 46.0% (128/278) cases with cross-contamination/misidentification were uncovered coming from 22 institutes. Notably, 73.2% (52 out of 71) of the cell lines established by the Chinese researchers were misidentified and accounted for 40.6% of total misidentification (52/128). Further, 67.3% (35/52) of the misidentified cell lines established in laboratories of China were HeLa cells or a possible hybrid of HeLa with another kind of cell line. Furthermore, the bile duct cancer cell line HCCC-9810 and degenerative lung cancer Calu-6 exhibited 88.9% match in the ATCC database (9-loci), indicating that they were from the same origin. However, when we used 21-loci to compare these two cell lines with the same algorithm, the percent match was only 48.2%, indicating that these two cell lines were different. The SNP profiles of HCCC-9810 and Calu-6 also revealed that they were different cell lines. 150 cell lines with unique profiles demonstrated a wide range of <i>in vitro</i> phenotypes. This panel of 150 genomically validated cancer cell lines represents a valuable resource for the cancer research community and will advance our understanding of the disease by providing a standard reference for cell lines that can be used for biological as well as preclinical studies.</p></div

Property Inference Attacks Against GANs

While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks. So far, most of the attacks in this field focus on discriminative models, represented by classifiers. Meanwhile, little attention has been paid to the security and privacy risks of generative models, such as generative adversarial networks (GANs). In this paper, we propose the first set of training dataset property inference attacks against GANs. Concretely, the adversary aims to infer the macro-level training dataset property, i.e., the proportion of samples used to train a target GAN with respect to a certain attribute. A successful property inference attack can allow the adversary to gain extra knowledge of the target GAN’s training dataset, thereby directly violating the intellectual property of the target model owner. Also, it can be used as a fairness auditor to check whether the target GAN is trained with a biased dataset. Besides, property inference can serve as a building block for other advanced attacks, such as membership inference. We propose a general attack pipeline that can be tailored to two attack scenarios, including the full black-box setting and partial black-box setting. For the latter, we introduce a novel optimization framework to increase the attack efficacy. Extensive experiments over four representative GAN models on five property inference tasks show that our attacks achieve strong performance. In addition, we show that our attacks can be used to enhance the performance of membership inference against GANs

The origin distribution of 150 unique cell lines.

<p>The origin distribution of 150 unique cell lines.</p

Teacher Model Fingerprinting Attacks Against Transfer Learning

Transfer learning has become a common solution to address training data scarcity in practice. It trains a specified student model by reusing or fine-tuning early layers of a well-trained teacher model that is usually publicly available. However, besides utility improvement, the transferred public knowledge also brings potential threats to model confidentiality, and even further raises other security and privacy issues. In this paper, we present the first comprehensive investigation of the teacher model exposure threat in the transfer learning context, aiming to gain a deeper insight into the tension between public knowledge and model confidentiality. To this end, we propose a teacher model fingerprinting attack to infer the origin of a student model, i.e., the teacher model it transfers from. Specifically, we propose a novel optimizationbased method to carefully generate queries to probe the student model to realize our attack. Unlike existing model reverse engineering approaches, our proposed fingerprinting method neither relies on fine-grained model outputs, e.g., posteriors, nor auxiliary information of the model architecture or training dataset. We systematically evaluate the effectiveness of our proposed attack. The empirical results demonstrate that our attack can accurately identify the model origin with few probing queries. Moreover, we show that the proposed attack can serve as a stepping stone to facilitating other attacks against machine learning models, such as model stealing

Amplifying Membership Exposure via Data Poisoning

As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples. To this end, we demonstrate a set of data poisoning attacks to amplify the membership exposure of the targeted class. We first propose a generic dirty-label attack for supervised classification algorithms. We then propose an optimization-based clean-label attack in the transfer learning scenario, whereby the poisoning samples are correctly labeled and look “natural” to evade human moderation. We extensively evaluate our attacks on computer vision benchmarks. Our results show that the proposed attacks can substantially increase the membership inference precision with minimum overall test-time model performance degradation. To mitigate the potential negative impacts of our attacks, we also investigate feasible countermeasures

Additional file 1 of E. urophylla × E. grandis high-quality genome and comparative genomics provide insights on evolution and diversification of eucalyptus

Supplementary Material

Additional file 3 of E. urophylla × E. grandis high-quality genome and comparative genomics provide insights on evolution and diversification of eucalyptus

Supplementary Material

Diagram of Chengdu water resources supply and demand system.

Diagram of Chengdu water resources supply and demand system.</p