256 research outputs found
Electric Field Effect in Multilayer Cr2Ge2Te6: a Ferromagnetic Two-Dimensional Material
The emergence of two-dimensional (2D) materials has attracted a great deal of
attention due to their fascinating physical properties and potential
applications for future nanoelectronic devices. Since the first isolation of
graphene, a Dirac material, a large family of new functional 2D materials have
been discovered and characterized, including insulating 2D boron nitride,
semiconducting 2D transition metal dichalcogenides and black phosphorus, and
superconducting 2D bismuth strontium calcium copper oxide, molybdenum
disulphide and niobium selenide, etc. Here, we report the identification of
ferromagnetic thin flakes of Cr2Ge2Te6 (CGT) with thickness down to a few
nanometers, which provides a very important piece to the van der Waals
structures consisting of various 2D materials. We further demonstrate the giant
modulation of the channel resistance of 2D CGT devices via electric field
effect. Our results illustrate the gate voltage tunability of 2D CGT and the
potential of CGT, a ferromagnetic 2D material, as a new functional quantum
material for applications in future nanoelectronics and spintronics.Comment: To appear in 2D Material
Detecting cyberattacks in industrial control systems using online learning algorithms
Industrial control systems are critical to the operation of industrial
facilities, especially for critical infrastructures, such as refineries, power
grids, and transportation systems. Similar to other information systems, a
significant threat to industrial control systems is the attack from
cyberspace---the offensive maneuvers launched by "anonymous" in the digital
world that target computer-based assets with the goal of compromising a
system's functions or probing for information. Owing to the importance of
industrial control systems, and the possibly devastating consequences of being
attacked, significant endeavors have been attempted to secure industrial
control systems from cyberattacks. Among them are intrusion detection systems
that serve as the first line of defense by monitoring and reporting potentially
malicious activities. Classical machine-learning-based intrusion detection
methods usually generate prediction models by learning modest-sized training
samples all at once. Such approach is not always applicable to industrial
control systems, as industrial control systems must process continuous control
commands with limited computational resources in a nonstop way. To satisfy such
requirements, we propose using online learning to learn prediction models from
the controlling data stream. We introduce several state-of-the-art online
learning algorithms categorically, and illustrate their efficacies on two
typically used testbeds---power system and gas pipeline. Further, we explore a
new cost-sensitive online learning algorithm to solve the class-imbalance
problem that is pervasive in industrial intrusion detection systems. Our
experimental results indicate that the proposed algorithm can achieve an
overall improvement in the detection rate of cyberattacks in industrial control
systems
Circular RNA circNOL10 Inhibits Lung Cancer Development by Promoting SCLM1-Mediated Transcriptional Regulation of the Humanin Polypeptide Family
circNOL10 is a circular RNA expressed at low levels in lung cancer, though its functions in lung cancer remain unknown. Here, the function and molecular mechanism of circNOL10 in lung cancer development are investigated using in vitro and in vivo studies, and it is shown that circNOL10 significantly inhibits the development of lung cancer and that circNOL10 expression is co‐regulated by methylation of its parental gene Pre‐NOL10 and by splicing factor epithelial splicing regulatory protein 1 (ESRP1). circNOL10 promotes the expression of transcription factor sex comb on midleg‐like 1 (SCML1) by inhibiting transcription factor ubiquitination and thus also affects regulation of the humanin (HN) polypeptide family by SCML1. circNOL10 also affects mitochondrial function through regulating the humanin polypeptide family and affecting multiple signaling pathways, ultimately inhibiting cell proliferation and cell cycle progression, and promoting the apoptosis of lung cancer cells, thereby inhibiting lung cancer development. This study investigates the functions and molecular mechanisms of circNOL10 in the development of lung cancer and reveals its involvement in the transcriptional regulation of the HN polypeptide family by SCML1. The results also demonstrate the inhibitory effect of HN on lung cancer cells growth. These findings may identify novel targets for the molecular therapy of lung cancer
Functions of exogenous FGF signals in regulation of fibroblast to myofibroblast differentiation and extracellular matrix protein expression
Fibroblasts are widely distributed cells found in most tissues and upon tissue injury, they are able to differentiate into myofibroblasts, which express abundant extracellular matrix (ECM) proteins. Overexpression and unordered organization of ECM proteins cause tissue fibrosis in damaged tissue. Fibroblast growth factor (FGF) family proteins are well known to promote angiogenesis and tissue repair, but their activities in fibroblast differentiation and fibrosis have not been systematically reviewed. Here we summarize the effects of FGFs in fibroblast to myofibroblast differentiation and ECM protein expression and discuss the underlying potential regulatory mechanisms, to provide a basis for the clinical application of recombinant FGF protein drugs in treatment of tissue damage
High-resolution face swapping via latent semantics disentanglement
We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure attributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending to produce a plausible swapping result. We further extend our method to video face swapping by enforcing two spatio-temporal constraints on the latent space and the image space. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency. Code can be found at: https://github.com/cnnlstm/FSLSD_HiRes
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