Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.Comment: Accepted by ICLR 202

Detecting single molecules inside a carbon nanotube to control molecular sequences using inertia trapping phenomenon

Here we show the detection of single gas molecules inside a carbon nanotube based on the change in resonance frequency and amplitude associated with the inertia trapping phenomenon. As its direct implication, a method for controlling the sequence of small molecule is then proposed to realize the concept of manoeuvring of matter atom by atom in one dimension. The detection as well as the implication is demonstrated numerically with the molecular dynamics method. It is theoretically assessed that it is possible for a physical model to be fabricated in the very near future

DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and membership inference. To protect against these attacks, differential privacy (DP) has become the de facto standard for privacy-preserving machine learning, particularly those popular training algorithms using stochastic gradient descent, such as DPSGD. Nonetheless, DPSGD still suffers from severe utility loss due to its slow convergence. This is partially caused by the random sampling, which brings bias and variance to the gradient, and partially by the Gaussian noise, which leads to fluctuation of gradient updates. Our key idea to address these issues is to apply selective updates to the model training, while discarding those useless or even harmful updates. Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model. As such, DPSUR ensures the training in the right direction and thus can achieve faster convergence than DPSGD. The main challenges lie in two aspects -- privacy concerns arising from gradient evaluation, and gradient selection strategy for model update. To address the challenges, DPSUR introduces a clipping strategy for update randomization and a threshold mechanism for gradient selection. Experiments conducted on MNIST, FMNIST, CIFAR-10, and IMDB datasets show that DPSUR significantly outperforms previous works in terms of convergence speed and model utility.Comment: This paper has been accepted by VLDB 202

hSef potentiates EGF-mediated MAPK signaling through affecting EGFR trafficking and degradation

Sef (similar expression to fgf genes) was identified as an effective antagonist of fibroblast growth factor (FGF) in vertebrates. Previous reports have demonstrated that Sef interacts with FGF receptors (FGFRs) and inhibits FGF signaling, however, its role in regulating epidermal growth factor receptor (EGFR) signaling remains unclear. In this report, we found that hSef localizes to the plasma membrane (PM) and is subjected to rapid internalization and well localizes in early/recycling endosomes while poorly in late endosomes/lysosomes. We observed that hSef interacts and functionally colocalizes with EGFR in early endosomes in response to EGF stimulation. Importantly, we demonstrated that overexpression of hSef attenuates EGFR degradation and potentiates EGF-mediated mitogen-activated protein kinase (MAPK) signaling by interfering EGFR trafficking. Finally, our data showed that, with overexpression of hSef, elevated levels of Erk phosphorylation and differentiation of rat pheochromocytoma (PC12) cells occur in response to EGF stimulation. Taken together, these data suggest that hSef plays a positive role in the EGFR-mediated MAPK signaling pathway. This report, for the first time, reveals opposite roles for Sef in EGF and FGF signalings

Multiwalled carbon nanotubes co-delivering sorafenib and epidermal growth factor receptor siRNA enhanced tumor-suppressing effect on liver cancer.

OBJECTIVE: This study aimed to investigate the effects of multiwalled carbon nanotubes (MWNTs) co-delivering sorafenib (Sor) and epidermal growth factor receptor (EGFR) siRNA (MWNT/Sor/siRNA) on tumor growth in liver cancer (LC). RESULTS: MWNT/Sor/siRNA was proved to possess increased Sor release, high siRNA stability, and enhanced cellular uptake. In addition, MWNT treatment has few effects on cell proliferation and apoptosis in HepG2 cells; however, MWNT/Sor/siRNA treatment significantly inhibited clone number and induced cell apoptosis, which shows a more favorable antitumor effect than MWNT/Sor and free Sor and free siRNA in HepG2 cells. Moreover MWNT/Sor/siRNA treatment has the most significant antitumor effect CONCLUSIONS: MWNT/Sor/siRNA exhibited a superior antitumor effect METHODS: The MWNT/Sor and MWNT/Sor/siRNA were prepared, and then the morphologies of MWNT/Sor/siRNA were analyzed

Templated Growth of Covalently Bonded Three-Dimensional Carbon Nanotube Networks Originated from Graphene

A template-assisted method that enables the growth of covalently bonded three-dimensional carbon nanotubes (CNTs) originating from graphene at a large scale is demonstrated. Atomic force microscopy-based mechanical tests show that the covalently bonded CNT structure can effectively distribute external loading throughout the network to improve the mechanical strength of the material

Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic Lake Donghu (Wuhan, China).

Uncovering which environmental factors govern community diversity patterns and how ecological processes drive community turnover are key questions related to understand the community assembly. However, the ecological mechanisms regulating long-term variations of bacterioplankton communities in lake ecosystems remain poorly understood. Here we present nearly a decade-long study of bacterioplankton communities from the eutrophic Lake Donghu (Wuhan, China) using 16S rRNA gene amplicon sequencing with MiSeq platform. We found strong repeatable seasonal diversity patterns in terms of both common (detected in more than 50% samples) and dominant (relative abundance >1%) bacterial taxa turnover. Moreover, community composition tracked the seasonal temperature gradient, indicating that temperature is a key environmental factor controlling observed diversity patterns. Total phosphorus also contributed significantly to the seasonal shifts in bacterioplankton composition. However, any spatial pattern of bacterioplankton communities across the main lake areas within season was overwhelmed by their temporal variabilities. Phylogenetic analysis further indicated that 75%-82% of community turnover was governed by homogeneous selection due to consistent environmental conditions within seasons, suggesting that the microbial communities in Lake Donghu are mainly controlled by niche-based processes. Therefore, dominant niches available within seasons might be occupied by similar combinations of bacterial taxa with modest dispersal rates throughout different lake areas