Non-coherent detection for ultraviolet communications with inter-symbol interference

Ultraviolet communication (UVC) serves as a promising supplement to share the responsibility for the overloads in conventional wireless communication systems. One challenge for UVC lies in inter-symbol-interference (ISI), which combined with the ambient noise, contaminates the received signals and thereby deteriorates the communication accuracy. Existing coherent signal detection schemes (e.g. maximum likelihood sequence detection, MLSD) require channel state information (CSI) to compensate the channel ISI effect, thereby falling into either a long overhead and large computational complexity, or poor CSI acquisition that further hinders the detection performance. Non-coherent schemes for UVC, although capable of reducing the complexity, cannot provide high detection accuracy in the face of ISI. In this work, we propose a novel non-coherent paradigm via the exploration of the UV signal features that are insensitive to the ISI. By optimally weighting and combining the extracted features to minimize the bit error rate (BER), the optimally-weighted non-coherent detection (OWNCD) is proposed, which converts the signal detection with ISI into a binary detection framework with a heuristic decision threshold. As such, the proposed OWNCD avoids the complex CSI estimation and guarantees the detection accuracy. Compared to the state-of-the-art MLSD in the cases of static and time-varying CSI, the proposed OWNCD can gain ∼1 dB and 8 dB in signal-to-noise-ratio (SNR) at the 7% overhead FEC limit (BER of 4.5×10 −3 , respectively, and can also reduce the computational complexity by 4 order of magnitud

Boundary Lipschitz Regularity and the Hopf Lemma on Reifenberg Domains for Fully Nonlinear Elliptic Equations

In this paper, we prove the boundary Lipschitz regularity and the Hopf Lemma by a unified method on Reifenberg domains for fully nonlinear elliptic equations. Precisely, if the domain $\Omega$ satisfies the exterior Reifenberg $C^{1,\mathrm{Dini}}$ condition at $x_0\in \partial \Omega$ (see Definition 1.3), the solution is Lipschitz continuous at $x_0$; if $\Omega$ satisfies the interior Reifenberg $C^{1,\mathrm{Dini}}$ condition at $x_0$ (see Definition 1.4), the Hopf lemma holds at $x_0$. Our paper extends the results under the usual $C^{1,\mathrm{Dini}}$ condition

Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Xanthomonas oryzae pv. oryzae TALE proteins recruit OsTFIIAγ1 to compensate for the absence of OsTFIIAγ5 in bacterial blight in rice

Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of bacterial blight (BB) of rice, uses transcription activator-like effectors (TALEs) to interact with the basal transcription factor gamma subunit OsTFIIAγ5 (Xa5) and activates the transcription of host genes. However, how OsTFIIAγ1, the other OsTFIIAγ protein, functions in the presence of TALEs remains unclear. In this study, we show that OsTFIIAγ1 plays a compensatory role in the absence of Xa5. The expression of OsTFIIAγ1, which is activated by TALE PthXo7, increases the expression of host genes targeted by avirulent and virulent TALEs. Defective OsTFIIAγ1 rice lines show reduced expression of the TALE-targeted susceptibility (S) genes, OsSWEET11 and OsSWEET14, which results in increased BB resistance. Selected TALEs (PthXo1, AvrXa7 and AvrXa27) were evaluated for interactions with OsTFIIAγ1, Xa5 and xa5 (naturally occurring mutant form of Xa5) using biomolecular fluorescence complementation (BiFC) and microscale thermophoresis (MST). BiFC and MST demonstrated that the three TALEs bind Xa5 and OsTFIIAγ1 with a stronger affinity than xa5. These results provide insights into the complex roles of OsTFIIAγ1 and OsTFIIAγ5 in TALE-mediated host gene transcription

Experimental study on the mechanical behavior of RPC filled square steel tube columns subjected to eccentric compression

In order to study the mechanical behavior of reactive powder concrete (RPC) filled square steel tubular columns, this paper designs an eccentric compression experiment for 12 specimens of RPC filled square steel tubular columns and studies the effects of failure form, load-displacement curve, slenderness ratio, steel ratio and eccentricity ratio of an eccentrically loaded columns on its mechanical behavior. At the same time, it also compares the experiment results with the bearing capacity calculated with relevant specifications and uses Abaqus to carry out numerical simulation of eccentrically loaded columns. The results show that, the failure form of the eccentrically loaded RPC filled square steel tubular column shows local buckling failure. Before the ultimate load is reached, there is no significant change on the surface of the specimen, and the yield stage of the load-displacement curve is not obvious, either. The results of the bearing capacity calculation formula recommended in CECS28-2012 are close to the experiment results and relatively conservative, so it is more applicable to the bearing capacity design of eccentrically loaded RPC filled square steel tubular columns. The results of the numerical simulation analysis are in good agreement with the experimental results, which can provide theoretical support for engineering practice