MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.Comment: Accepted to TPAMI in September, 2019. v6 updates: correct units in Fig. 11; correct author info; delete bio photos. arXiv admin note: text overlap with arXiv:1803.0468

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance. However, it still remains a crucial challenge to train a reliable detector working well in different multispectral pedestrian datasets without manual annotations. In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain. Pseudo annotations are generated using the detector trained on source domain, and then updated by fixing the parameters of detector and minimizing the cross entropy loss without back-propagation. Training labels are generated using the pseudo annotations by considering the characteristics of similarity and complementarity between well-aligned visible and infrared image pairs. The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with back-propagation. The optimal parameters of detector can be obtained after iteratively updating the pseudo annotations and parameters. Experimental results show that our proposed unsupervised multimodal domain adaptation method achieves significantly higher detection performance than the approach without domain adaptation, and is competitive with the supervised multispectral pedestrian detectors

Chirp spread spectrum toward the Nyquist signaling rate - orthogonality condition and applications

With the proliferation of Internet-of-Things (IoT), the chirp spread spectrum (CSS) technique is re-emerging for communications. Although CSS can offer high processing gain, its poor spectral efficiency and the lack of orthogonality among different chirps tend to limit its potential. In this paper, we derive the condition to orthogonally multiplex an arbitrary number of linear chirps. For the first time in the literature, we show that the maximum modulation rate of the linear continuous-time chirps satisfying the orthogonality condition can approach the Nyquist signaling rate, the same as single-carrier waveforms with Nyquist signaling or orthogonal frequency-division multiplexing signals. The performance of the proposed orthogonal CSS is analyzed in comparison to the emerging LoRa systems for IoT applications with power constraint, and its capability for high-speed communications is also demonstrated in the sense of Nyquist signaling

Observation of quantum fingerprinting beating the classical limit

Quantum communication has historically been at the forefront of advancements, from fundamental tests of quantum physics to utilizing the quantum-mechanical properties of physical systems for practical applications. In the field of communication complexity, quantum communication allows the advantage of an exponential reduction in the information transmitted over classical communication to accomplish distributed computational tasks. However, to date, demonstrating this advantage in a practical setting continues to be a central challenge. Here, we report an experimental demonstration of a quantum fingerprinting protocol that for the first time surpasses the ultimate classical limit to transmitted information. Ultra-low noise superconducting single-photon detectors and a stable fibre-based Sagnac interferometer are used to implement a quantum fingerprinting system that is capable of transmitting less information than the classical proven lower bound over 20 km standard telecom fibre for input sizes of up to two Gbits. The results pave the way for experimentally exploring the advanced features of quantum communication and open a new window of opportunity for research in communication complexity and testing the foundations of physics.Comment: 19 pages, 4 figure

MAP4K family kinases act in parallel to MST1/2 to activate LATS1/2 in the Hippo pathway.

The Hippo pathway plays a central role in tissue homoeostasis, and its dysregulation contributes to tumorigenesis. Core components of the Hippo pathway include a kinase cascade of MST1/2 and LATS1/2 and the transcription co-activators YAP/TAZ. In response to stimulation, LATS1/2 phosphorylate and inhibit YAP/TAZ, the main effectors of the Hippo pathway. Accumulating evidence suggests that MST1/2 are not required for the regulation of YAP/TAZ. Here we show that deletion of LATS1/2 but not MST1/2 abolishes YAP/TAZ phosphorylation. We have identified MAP4K family members--Drosophila Happyhour homologues MAP4K1/2/3 and Misshapen homologues MAP4K4/6/7-as direct LATS1/2-activating kinases. Combined deletion of MAP4Ks and MST1/2, but neither alone, suppresses phosphorylation of LATS1/2 and YAP/TAZ in response to a wide range of signals. Our results demonstrate that MAP4Ks act in parallel to and are partially redundant with MST1/2 in the regulation of LATS1/2 and YAP/TAZ, and establish MAP4Ks as components of the expanded Hippo pathway