In this work, we describe an updated version of single arm locking, and the noise amplification due to the nulls can be flexibly restricted with the help of optical frequency comb. We show that, the laser phase noise can be divided by a specific fact
or with optical frequency comb as the bridge. The analytical results indicate that, the peaks in the science band have been greatly reduced. The performance of the noise suppression shows that the total noise after arm locking can well satisfy the requirement of time delay interferometry, even with the free-running laser source. We also estimate the frequency pulling characteristics of the updated single arm locking, and the results suggest that the pulling rate can be tolerated, without the risk of mode hopping. Arm locking will be a valuable solution for the noise reduction in the space-borne GW detectors. We demonstrate that, with the precise control of the returned laser phase noise, the noise amplification in the science band can be efficiently suppressed based on the updated single arm locking. Not only our method allows the suppression of the peaks, the high gain, low pulling rate, it can also serve for full year, without the potential risk of locking failure due to the arm length mismatch. We finally discuss the unified demonstration of the updated single arm locking, where both the local and the returned laser phase noises can be tuned to generate the expected arm-locking sensor actually. Our work could provide a powerful method for the arm locking in the future space-borne GW detectors.
The ongoing development of the space-based laser interferometer missions is aiming at unprecedented gravitational wave detections in the millihertz frequency band. The spaceborne nature of the experimental setups leads to a degree of subtlety regardi
ng the otherwise overwhelming laser frequency noise. The cancellation of the latter is accomplished through the time-delay interferometry technique. Moreover, to eventually achieve the desired noise level, the phase fluctuations of the onboard ultra-stable oscillator must also be suppressed. This can be fulfilled by introducing sideband signals which, in turn, give rise to an improved cancellation scheme accounting for the clock-jitter noise. Nonetheless, for certain Sagnac-type interferometry layouts, it can be shown that resultant residual clock noise found in the literature can be further improved. In this regard, we propose refined cancellation combinations for two specific clock noise patterns. This is achieved by employing the so-called geometric time-delay interferometry interpretation. It is shown that for specific Sagnac combinations, the residual noise diminishes significantly to attain the experimentally acceptable sensitivity level. Moreover, we argue that the derived combination, in addition to the existing ones in the literature, furnishes a general-purpose cancellation scheme that serves for arbitrary time-delay interferometry combinations. The subsequential residual noise will only involve factors proportional to the commutators between the delay operators. Our arguments reside in the form of the clock noise expressed in terms of the coefficients of the generating set of the first module of syzygies, the linear combination of which originally constitutes the very solution for laser noise reduction.
Current neuroscience focused approaches for evaluating the effectiveness of a design do not use direct visualisation of mental activity. A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG)
signals, recorded while subjects looked at 50 categories of images. A generative adversarial network (GAN) conditioned on the EEG latent representation is trained for reconstructing these images. After training, the neural network is able to reconstruct images from brain activity recordings. To demonstrate the proposed method in the context of the mental association with a design, we performed a study that indicates an iconic design image could inspire the subject to create cognitive associations with branding and valued products. The proposed method could have the potential in verifying designs by visualizing the cognitive understanding of underlying brain activity.
With the rapid development of Green Communication Network, the types and quantity of network traffic data are accordingly increasing. Network traffic classification become a non-trivial research task in the area of network management and security, wh
ich not only help to improve the fine-grained network resource allocation, but also enable policy-driven network management. Meanwhile, the combination of SDN and Edge Computing can leverage both SDN at its global visiability of network-wide and Edge Computing at its low latency and good privacy-preserving. However, capturing large labeled datasets is a cumbersome and time-consuming manual labor. Semi-Supervised learning is an appropriate technique to overcome this problem. With that in mind, we proposed a Generative Adversarial Network (GAN)-based Semi-Supervised Learning Encrypted Traffic Classification method called emph{ByteSGAN} embedded in SDN Edge Gateway to achieve the goal of traffic classification in a fine-grained manner to further improve network resource utilization. ByteSGAN can only use a small number of labeled traffic samples and a large number of unlabeled samples to achieve a good performance of traffic classification by modifying the structure and loss function of the regular GAN discriminator network in a semi-supervised learning way. Based on public dataset ISCX2012 VPN-nonVPN, two experimental results show that the ByteSGAN can efficiently improve the performance of traffic classifier and outperform the other supervised learning method like CNN.
Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology. However, real-world image prior are often hard to simulate with precise mathematical models, which
inevitably limits the performance and generalization ability of existing prior-regularized restoration methods. In this paper, we study the problem of face restoration under a more practical ``dual blind setting, i.e., without prior assumptions or hand-crafted regularization terms on the degradation profile or image contents. To this end, a novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration, with two key elements: 1) an implicit formulation to circumvent the ill-defined restoration mapping and 2) a subspace prior decomposition and fusion mechanism to dynamically handle inputs at varying degradation levels with consistent high-quality restoration results. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods for a variety of restoration subtasks, including a 3.69db PSNR and 45.8% FID gain against ESRGAN, the 2018 NTIRE SR challenge winner. Overall, we prove that it is possible to capture and utilize prior knowledge without explicitly formulating it, which will help inspire new research paradigms towards low-level vision tasks.
We investigate mixed-valence oxide Co$_3$O$_4$ using Co $2p3d$ resonant inelastic X-ray scattering (RIXS). By setting resonant edges at Co$^{2+}$ and Co$^{3+}$ ions, the $dd$ excitations on the two Co sites are probed selectively, providing detailed
information on the local electronic structure of Co$_3$O$_4$. The $2p3d$ RIXS result reveals the $^4$T$_{2}$ excited state of tetrahedral Co$^{2+}$ site at 0.5 eV beyond the discriminative power of optical absorption spectroscopies. Additionally, the $^3$T$_{2g}$ excited stated at 1.3 eV is uniquely identified for the octahedral Co$^{3+}$ site. Guided by cluster multiplet simulations, the ground-state character of the Co$^{2+}$ and Co$^{3+}$ site is determined to be high-spin $^4$A$_{2}$(T$_d$) and low-spin $^1$A$_{1g}$(O$_h$), respectively. This indicates that only the Co$^{2+}$ site is magnetically active site at low-temperatures in Co$_3$O$_4$. The ligand-to-metal charge transfer analysis suggests a formation of a strong covalent bonding between Co and O ions at the Co$^{3+}$ site, while Co$^{2+}$ is rather ionic.
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for prod
uction quality and user engagement. However, existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency, which severely degrade the visual quality and realism of generated images. Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment. Concretely, we analyze the potential design flaws of existing methods via an illustrative example, and establish the core FHPT methodology by combing the idea of content synthesis and feature transfer together in a mutually-guided fashion. Thereafter, we substantiate the proposed methodology with a Detail Replenishing Network (DRN) and a corresponding coarse-to-fine model training scheme. Moreover, we build up a complete suite of fine-grained evaluation protocols to address the challenges of FHPT in a comprehensive manner, including semantic analysis, structural detection and perceptual quality assessment. Extensive experiments on the DeepFashion benchmark dataset have verified the power of proposed benchmark against start-of-the-art works, with 12%-14% gain on top-10 retrieval recall, 5% higher joint localization accuracy, and near 40% gain on face identity preservation. Moreover, the evaluation results offer further insights to the subject matter, which could inspire many promising future works along this direction.
The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control. The requirements of the IoT applications vary due to different tasks in the smart grid. In this paper, we propose a new computi
ng paradigm to offer location-aware, latencysensitive monitoring and intelligent control for IoT applications in the smart grid. In particular, a new fog-based architecture and programming model is designed. Fog computing extends computing to the edge of a network, which has a perfect match to IoT applications. However, existing schemes can hardly satisfy the distributed coordination within fog computing nodes in the smart grid. In the proposed model, we introduce a new distributed fog computing coordinator, which periodically gathers information of fog computing nodes, e.g., remaining resources, tasks, etc. Moreover, the fog computing coordinator also manages jobs so that all computing nodes can collaborate on complex tasks. In addition, we construct a working prototype of intelligent electric vehicle service to evaluate the proposed model. Experiment results are also presented to demonstrate that our proposed model exceed the traditional fog computing schemes for IoT applications in the smart grid.
More than 50 years ago the electron-hole attraction was proposed to drive narrow gap semiconductors or semimetals to a new phase, the excitonic insulator. The experimental proof of its existence in bulk materials remains elusive. In strongly correlat
ed insulators, the proximity of the excitonic insulator phase is reflected by the presence of dispersive electron-hole excitations with a small gap above a singlet ground state. Recently, such an excitation spectrum was proposed to be realized in perovskite oxide LaCoO$_3$. In this Letter we use Co $L_3$-edge resonant inelastic X-ray scattering to put this proposal to experimental test.
