Transcriptional profiling of CcpE-regulated genes in Staphylococcus aureus

AbstractThe transcriptional regulator CcpE is an important citrate-sensing regulator that modulates metabolic state, virulence factor expression, and bacterial virulence of Staphylococcus aureus (Ding et al., 2014 [1]). In this article, we report detailed methods for genome-wide transcriptional profiling of CcpE-regulated genes generated for the research article “Metabolic sensor governing bacterial virulence in Staphylococcus aureus” (Ding et al., 2014 [1]). All transcriptional profiling data was deposited to Gene Expression Omnibus (GEO) database under accession number GSE57260

DIFFUSION APPROXIMATION FOR EFFICIENCY-DRIVEN QUEUES UNDER REFINED PATIENCE TIME SCALING

Ph.DDOCTOR OF PHILOSOPH

PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks

Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collaborative inference, the edge device first feeds the input to a partial DNN locally and then uploads the intermediate result to the cloud to complete the inference. However, recent research indicates model inversion attacks (MIAs) can reconstruct input data from intermediate results, posing serious privacy concerns for collaborative inference. Existing perturbation and cryptography techniques are inefficient and unreliable in defending against MIAs while performing accurate inference. This paper provides a viable solution, named PATROL, which develops privacy-oriented pruning to balance privacy, efficiency, and utility of collaborative inference. PATROL takes advantage of the fact that later layers in a DNN can extract more task-specific features. Given limited local resources for collaborative inference, PATROL intends to deploy more layers at the edge based on pruning techniques to enforce task-specific features for inference and reduce task-irrelevant but sensitive features for privacy preservation. To achieve privacy-oriented pruning, PATROL introduces two key components: Lipschitz regularization and adversarial reconstruction training, which increase the reconstruction errors by reducing the stability of MIAs and enhance the target inference model by adversarial training, respectively

Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. Despite the empirical success, general theoretical guarantees of convergence on this method remain an open question. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time. In particular, we prove that under certain sufficient conditions and for both IID and non-IID data, these algorithms converge to a stationary point of standard FL for general smooth cost functions. Moreover, we introduce the concept of minimum coverage index, together with model reduction noise, which will determine the convergence of heterogeneous federated learning, and therefore we advocate for a holistic approach that considers both factors to enhance the efficiency of heterogeneous federated learning.Comment: Accepted at NeurIPS 202

Effect of Rice Fissure on Taste Quality of Cooked Rice

According to the change of texture attribute of cooked-rice under different fissure rate of rice, the relationship between fissure rate of rice and taste value of cooked rice were studied using correlation analysis and path analysis methods. The data of correlation analysis showed that the influence of texture attribute was significant on taste. Fissure rate had an effect on taste through hardness, gumminess, chewiness, and springiness. The data of path analysis suggested that the direct effect of gumminess was significant on taste, and the other indicators of texture attribute had indirect effect through gumminess. A regression model was constructed based on the indicators viz fissure rate, texture attributes