FedRecAttack: Model Poisoning Attack to Federated Recommendation

Federated Recommendation (FR) has received considerable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from private user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.Comment: This paper has been accepted by IEEE International Conference on Data Engineering 2022 (Second Research Round

Malondialdehyde Suppresses Cerebral Function by Breaking Homeostasis between Excitation and Inhibition in Turtle Trachemys scripta

The levels of malondialdehyde (MDA) are high in the brain during carbonyl stress, such as following daily activities and sleep deprivation. To examine our hypothesis that MDA is one of the major substances in the brain leading to fatigue, the influences of MDA on brain functions and neuronal encodings in red-eared turtle (Trachemys scripta) were studied. The intrathecal injections of MDA brought about sleep-like EEG and fatigue-like behaviors in a dose-dependent manner. These changes were found associated with the deterioration of encoding action potentials in cortical neurons. In addition, MDA increased the ratio of γ-aminobutyric acid to glutamate in turtle's brain, as well as the sensitivity of GABAergic neurons to inputs compared to excitatory neurons. Therefore, MDA, as a metabolic product in the brain, may weaken cerebral function during carbonyl stress through breaking the homeostasis between excitatory and inhibitory neurons

Advances in multifield and multiscale coupling of rock engineering

No abstract available

Supramolecular Click Chemistry for Surface Modification of Quantum Dots Mediated by Cucurbit[7]uril

Cucurbiturils (CBs), barrel-shaped macrocyclic molecules, are capable of self-assembling at the surface of nanomaterials in their native state, via their carbonyl-ringed portals. However, the symmetrical two-portal structure typically leads to aggregated nanomaterials. We demonstrate that fluorescent quantum dot (QD) aggregates linked with CBs can be broken-up, retaining CBs adsorbed at their surface, via inclusion of guests in the CB cavity. Simultaneously, the QD surface is modified by a functional tail on the guest, thus the high affinity host-guest binding (logKa > 9) enables a non-covalent, click-like modification of the nanoparticles in aqueous solution. We achieved excellent modification efficiency in several functional QD conjugates as protein labels. Inclusion of weaker-binding guests (logKa = 4-6) enables subsequent displacement with stronger binders, realising modular switchable surface chemistries. Our general "hook-and-eye" approach to host-guest chemistry at nanomaterial interfaces will lead to divergent routes for nano-architectures with rich functionalities for theranostics and photonics in aqueous systems

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively

An FPGA-Based LSTM Acceleration Engine for Deep Learning Frameworks

Over the past two decades, Long Short-Term Memory (LSTM) networks have been used to solve problems that require modeling of long sequence because they can selectively remember certain patterns over a long period, thus outperforming traditional feed-forward neural networks and Recurrent Neural Network (RNN) on learning long-term dependencies. However, LSTM is characterized by feedback dependence, which limits the high parallelism of general-purpose processors such as CPU and GPU. Besides, in terms of the energy efficiency of data center applications, the high consumption of GPU and CPU computing cannot be ignored. To deal with the above problems, Field Programmable Gate Array (FPGA) is becoming an ideal alternative. FPGA has the characteristics of low power consumption and low latency, which are helpful for the acceleration and optimization of LSTM and other RNNs. This paper proposes an implementation scheme of the LSTM network acceleration engine based on FPGA and further optimizes the implementation through fixed-point arithmetic, systolic array and lookup table for nonlinear function. On this basis, for easy deployment and application, we integrate the proposed acceleration engine into Caffe, one of the most popular deep learning frameworks. Experimental results show that, compared with CPU and GPU, the FPGA-based acceleration engine can achieve performance improvement of 8.8 and 2.2 times and energy efficiency improvement of 16.9 and 9.6 times, respectively, within Caffe framework

A Novel Energy Function-Based Stability Evaluation and Nonlinear Control Approach for Energy Internet

Unlike conventional interconnected power systems, energy Internet presents an unsolved and more challenging problem for the society including transfer impedance, damping, large penetration of distributed generation, and numerous hybrid integration of generators and converters. In this paper, a novel energy function designed for energy internet router is proposed to accurately evaluate its transfer stability. The reliability of the proposed energy function is confirmed through both theoretical analysis and empirical simulations. Furthermore, generalized methods to determine critical stable energy, stable domain, and critical clearing time are proposed. By updating stability criterion and evaluating system energy of post-disturbance system, fault energy-based impulsive feedback control method is specifically designed for energy Internet to stabilize the system. Simulation and experimental results are provided to validate the effectiveness of the proposed energy function and nonlinear control method

Imaging of anti-inflammatory effects of HNO via a near-infrared fluorescent probe in cells and in rat gouty arthritis model

Nitroxyl (HNO) plays a crucial role in anti-inflammatory effects via the inhibition of inflammatory pathways, but the details of the endogenous generation of HNO still remain challenging owing to the complex biosynthetic pathways, in which the interaction between H2S and NO simultaneously generates HNO and polysulfides (H2Sn) in mitochondria. Moreover, nearly all the available fluorescent probes for HNO are utilized for imaging HNO in cells and tissues, instead of the in situ real-time detection of the simultaneous formation of HNO and H2Sn in mitochondria and animals. Here, we have developed a mitochondria-targeting near-infrared fluorescent probe, namely, Mito-JN, to detect the generation of HNO in cells and a rat model. The probe consists of three moieties: Aza-BODIPY as a fluorescent signal transducer, a triphenylphosphonium cation as a mitochondria-targeting agent, and a diphenylphosphinobenzoyl group as an HNO-responsive unit. The response mechanism is based on an aza-ylide intramolecular ester aminolysis reaction with fluorescence emissions on. Mito-JN displays high selectivity and sensitivity for HNO over various other biologically relevant species. Mito-JN was successfully used for the detection of the endogenous generation of HNO, which is derived from the crosstalk between H2S and NO in living cells. The additional generation of H2Sn was also confirmed using our previous probe Cy-Mito. The anti-inflammatory effect of HNO was examined in a cell model of LPS-induced inflammation and a rat model of gouty arthritis. The results imply that our probe is a good candidate for the assessment of the protective effects of HNO in inflammatory processes