Misconfiguration Checking for SDN: Data Structure, Theory and Algorithms

International audienc

Misconfiguration-Free Compositional SDN for Cloud Networks

International audienc

Fast Online Packet Classification With Convolutional Neural Network

International audienc

Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving

End-to-end autonomous driving has made impressive progress in recent years. Existing methods usually adopt the decoupled encoder-decoder paradigm, where the encoder extracts hidden features from raw sensor data, and the decoder outputs the ego-vehicle's future trajectories or actions. Under such a paradigm, the encoder does not have access to the intended behavior of the ego agent, leaving the burden of finding out safety-critical regions from the massive receptive field and inferring about future situations to the decoder. Even worse, the decoder is usually composed of several simple multi-layer perceptrons (MLP) or GRUs while the encoder is delicately designed (e.g., a combination of heavy ResNets or Transformer). Such an imbalanced resource-task division hampers the learning process. In this work, we aim to alleviate the aforementioned problem by two principles: (1) fully utilizing the capacity of the encoder; (2) increasing the capacity of the decoder. Concretely, we first predict a coarse-grained future position and action based on the encoder features. Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly. We also retrieve the encoder features around the predicted coordinate to obtain fine-grained information about the safety-critical region. Finally, based on the predicted future and the retrieved salient feature, we refine the coarse-grained position and action by predicting its offset from ground-truth. The above refinement module could be stacked in a cascaded fashion, which extends the capacity of the decoder with spatial-temporal prior knowledge about the conditioned future. We conduct experiments on the CARLA simulator and achieve state-of-the-art performance in closed-loop benchmarks. Extensive ablation studies demonstrate the effectiveness of each proposed module.Comment: Accepted by CVPR 202

O₂ Plasma Alternately Treated ALD-Al₂O₃ as Gate Dielectric for High Performance AlGaN/GaN MIS-HEMTs

This article systematically studies the AlGaN/GaN MIS-HEMTs using the O2 plasma alternately treated Al2O3 as gate dielectric. The X-ray photoelectron spectroscopy (XPS) analyses and capacitance-voltage (C-V) measurement results show that the density of the border traps originating from the Al-OH bonds in the ALD-Al2O3 gate dielectric can be significantly reduced after the O2 plasma alternating treatment. Consequently, a low gate leakage current and a high field-effect mobility of 1680cm2/ $\text{V}\cdot \text{s}$ are achieved. The results also demonstrate that the fabricated AlGaN/GaN MIS-HEMTs with the O2 plasma alternating treatment exhibit improved performances, having a high ON/OFF ratio of ~1011, a steep subthreshold slope of 74 mV/dec, a small hysteresis ( $\Delta V_{\mathrm {TH}}$ ) of 0.1 V and small ON-resistance ( $R_{\mathrm {ON}}$ ) of $6.0~\Omega \cdot$ mm. The device thermal stability was also improved within the tested temperature range. In addition, the pulsed $I_{\mathrm {D}}$ - $V_{\mathrm {DS}}$ measurements with quiescent drain bias ( $V_{\mathrm {DS0}}$ ) stress of 40 V present negligible current collapse (2%) and low degradation of dynamic $R_{\mathrm {ON}}$ by 1.04 times the static ${R} _{\mathrm {ON}}$

Growth substrates alter aboveground plant microbial and metabolic properties thereby influencing insect herbivore performance

The gut microbiome of plant-eaters is affected by the food they eat, but it is currently unclear how the plant metabolome and microbiome are influenced by the substrate the plant grows in and how this subsequently impacts the feeding behavior and gut microbiomes of insect herbivores. Here, we use Plutella xylostella caterpillars and show that the larvae prefer leaves of cabbage plants growing in a vermiculite substrate to those from plants growing in conventional soil systems. From a plant metabolomics analysis, we identified 20 plant metabolites that were related to caterpillar feeding performance. In a bioassay, the effects of these plant metabolites on insects’ feeding were tested. Nitrate and compounds enriched with leaves of soilless cultivation promoted the feeding of insects, while compounds enriched with leaves of plants growing in natural soil decreased feeding. Several microbial groups (e.g., Sporolactobacillus, Haliangium) detected inside the plant correlated with caterpillar feeding performance and other microbial groups, such as Ramlibacter and Methylophilus, correlated with the gut microbiome. Our results highlight the role of growth substrates on the food metabolome and microbiome and on the feeding performance and the gut microbiome of plant feeders. It illustrates how belowground factors can influence the aboveground properties of plant-animal systems, which has important implications for plant growth and pest control

High Color Conversion Efficiency Realized in Graphene-Connected Nanorod Micro-Light-Emitting Diodes with Hybrid Ag Nanoparticles and Quantum Dots Using Non-Radiative Energy Transfer and Localized Surface Plasmons

As a medium for color conversion, quantum dots (QDs) can be employed in the display of full-color GaN-based Micro-Light-Emitting Diodes (µLEDs) arrays. Typically, in a system where QDs are excited by UV/blue µLEDs, QDs are coated onto the LED surface. However, due to inherent defects in QDs and significant energy loss associated with this method, the color conversion efficiency (CCE) is suboptimal. In this paper, we introduce an innovative approach where we etch a uniform nanorod (NR) array onto the surface of µLEDs. We then mix Ag nanoparticles (NPs) with QDs to fill the gaps between the nanorods. Simultaneously, we utilize the excellent conductivity, transparency, and high strength of graphene to create a transparent conductive electrode on the nanopillar surface. This electrode serves to connect individual nanorods and enhance current spreading. The nanorod array's structure significantly reduces the distance between the QDs and the quantum well (QW), reducing energy loss from the excitation light source through a non-radiative energy transfer (NRET) mechanism. Additionally, the Ag NPs function as local surface plasmons (LSPs) in luminescent systems, further enhancing the CCE of QDs via the NRET mechanism. In this study, we compare the effects of two types of Ag NPs with different absorption resonance peaks on device performance. Our results demonstrate that Ag NPs with absorption resonance peaks matching the emission wavelength of QDs play a more crucial role in the composite system. This configuration achieves a CCE of 77.78% for µLEDs with nanorod arrays, operating at a current of 10 mA. Compared to the conventional planar µLED structure with QDs spin-coated on the surface, our proposed method improves the CCE of µLEDs by an impressive 285%. This outcome underscores the significant contribution of the NR structure and LSPs in enhancing the CCE of QD- µLEDs

Tapping the rhizosphere metabolites for the prebiotic control of soil-borne bacterial wilt disease

Abstract Prebiotics are compounds that selectively stimulate the growth and activity of beneficial microorganisms. The use of prebiotics is a well-established strategy for managing human gut health. This concept can also be extended to plants where plant rhizosphere microbiomes can improve the nutrient acquisition and disease resistance. However, we lack effective strategies for choosing metabolites to elicit the desired impacts on plant health. In this study, we target the rhizosphere of tomato (Solanum lycopersicum) suffering from wilt disease (caused by Ralstonia solanacearum) as source for potential prebiotic metabolites. We identify metabolites (ribose, lactic acid, xylose, mannose, maltose, gluconolactone, and ribitol) exclusively used by soil commensal bacteria (not positively correlated with R. solanacearum) but not efficiently used by the pathogen in vitro. Metabolites application in the soil with 1 µmol g−1 soil effectively protects tomato and other Solanaceae crops, pepper (Capsicum annuum) and eggplant (Solanum melongena), from pathogen invasion. After adding prebiotics, the rhizosphere soil microbiome exhibits enrichment of pathways related to carbon metabolism and autotoxin degradation, which were driven by commensal microbes. Collectively, we propose a novel pathway for mining metabolites from the rhizosphere soil and their use as prebiotics to help control soil-borne bacterial wilt diseases