Silicon photonic subsystem for broadband and RoF detection while enabling carrier reuse

We experimentally validate a silicon photonic subsystem designed for passive optical networks with carrier reuse. The subsystem is intended for future wavelength division multiplexed (WDM) PONs. It enables radio-over-fiber signals to cohabit an assigned wavelength slot without perturbing the PON signal, and conserving carrier power for the uplink. A microring modulator remodulates the residual carrier for the RoF uplink. We successfully detected the dropped an 8 GHz broadband signal and five 125 MHz radio-over-fiber signals. Two 125 MHz radio over fiber signals are remodulated onto the carrier. The uplink signal shows good performance, validating the residual downlink signals have been well rejected by the microring filters. The subsystem conserves a clean carrier for remodulation with good signal-to-carrier ratio

SiP-based SSBI cancellation for OFDM

We propose for the first time to use a silicon photonics (SiP) solution for a passive optical network to both reduce signal-signal beat interference (SSBI) and recuperate a part of the downlink carrier for use in the uplink. The Kramers-Kronig (KK) receiver for direct detection of advanced modulation formats overcomes SSBI at the cost of a moderate carrier to signal ratio (>6 dB) and high oversampling (4X). We propose an optical SSBI solution that achieves better performance than KK and requires only standard sampling and low (3 dB) carrier to signal power ratio. The receiver is conceived for the downlink in passive optical networks, where carrier signal must be husbanded for re-use in the uplink. Using cost effective and power efficient SiP, the receiver filters the incoming signal, suppresses SSBI, and routes a portion of the carrier for use in the uplink. We experimentally examine the SSBI suppression in this paper. While previous demonstrations used bulky, discrete components, we achieve significant Q-factor improvement with a simple SiP solution. We examine the optimal frequency offset between the carrier and the microring resonator center frequency. The robustness to frequency drift, as well as the impact of imperfect filtering, is discussed and quantified

Polarization-insensitive silicon microring modulator for single sideband modulation

We propose and experimentally demonstrate a polarization-insensitive single sideband modulator based on silicon microring modulators (MRM). The proposed modulator splits and modulates the two orthogonal polarization states of an input laser in a loopback structure, with an on-chip silicon polarization splitter rotator (PSR), overcoming the polarization dependence of the silicon photonic modulator. The IQ configuration of the modulator enables single sideband modulation, thus improving the resistance of the modulated signal to chromatic dispersion and extending the transmission reach. The adoption of an MRM relieves the bandwidth limitation in polarizationdiverse versions of SiP Mach-Zehnder modulators (MZM). Our experiments validate the proposed modulator polarization insensitivity and transmission performanc

The effect of ride experience on changing opinions toward autonomous vehicle safety

Autonomous vehicles (AVs) are a promising emerging technology that is likely to be widely deployed in the near future. People\u27s perception on AV safety is critical to the pace and success of deploying the AV technology. Existing studies found that people\u27s perceptions on emerging technologies might change as additional information was provided. To investigate this phenomenon in the AV technology context, this paper conducted real-world AV experiments and collected factors that may associate with people\u27s initial opinions without any AV riding experience and opinion change after a successful AV ride. A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people\u27s initial opinions and opinion change. The study found that people\u27s initial opinions toward AV safety are significantly associated with people\u27s age, personal income, monthly fuel cost, education experience, and previous AV experience. Further, the factors dominating people\u27s opinion change after a successful AV ride include people\u27s age, personal income, monthly fuel cost, daily commute time, driving alone indicator, willingness to pay for AV technology, and previous AV experience. These results provide important references for future implementations of the AV technology. Additionally, based on the inconsistent effects for variables across different models, suggestions for future transportation survey designs are provided

Semi-supervised co-clustering on attributed heterogeneous information networks

trueThe embargo period should be 2 years -- not sure why under the drop down I can only select one year. Please validate.</p

Multi-level Gated Bayesian Recurrent Neural Network for State Estimation

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely a multi-level gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian multi-level gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods

Kruppel-Like Factor 4-Dependent Staufen1-Mediated mRNA Decay Regulates Cortical Neurogenesis

Kruppel-like factor 4 (Klf4) is a zinc-finger-containing protein that plays a critical role in diverse cellular physiology. While most of these functions attribute to its role as a transcription factor, it is postulated that Klf4 may play a role other than transcriptional regulation. Here we demonstrate that Klf4 loss in neural progenitor cells (NPCs) leads to increased neurogenesis and reduced self-renewal in mice. In addition, Klf4 interacts with RNA-binding protein Staufen1 (Stau1) and RNA helicase Ddx5/17. They function together as a complex to maintain NPC self-renewal. We report that Klf4 promotes Stau1 recruitment to the 3′-untranslated region of neurogenesis-associated mRNAs, increasing Stau1-mediated mRNA decay (SMD) of these transcripts. Stau1 depletion abrogated SMD of target mRNAs and rescued neurogenesis defects in Klf4-overexpressing NPCs. Furthermore, Ddx5/17 knockdown significantly blocked Klf4-mediated mRNA degradation. Our results highlight a novel molecular mechanism underlying stability of neurogenesis-associated mRNAs controlled by the Klf4/Ddx5/17/Stau1 axis during mammalian corticogenesis