Double spike Dirichlet priors for structured weighting

Assigning weights to a large pool of objects is a fundamental task in a wide variety of applications. In this article, we introduce a concept of structured high-dimensional probability simplexes, whose most components are zero or near zero and the remaining ones are close to each other. Such structure is well motivated by 1) high-dimensional weights that are common in modern applications, and 2) ubiquitous examples in which equal weights -- despite their simplicity -- often achieve favorable or even state-of-the-art predictive performances. This particular structure, however, presents unique challenges both computationally and statistically. To address these challenges, we propose a new class of double spike Dirichlet priors to shrink a probability simplex to one with the desired structure. When applied to ensemble learning, such priors lead to a Bayesian method for structured high-dimensional ensembles that is useful for forecast combination and improving random forests, while enabling uncertainty quantification. We design efficient Markov chain Monte Carlo algorithms for easy implementation. Posterior contraction rates are established to provide theoretical support. We demonstrate the wide applicability and competitive performance of the proposed methods through simulations and two real data applications using the European Central Bank Survey of Professional Forecasters dataset and a UCI dataset

Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women

The detailed numbers of subjects from different centers. (DOCX 17.3 kb

Solar window blinds with passive cooling coating and smart controllers

In the recent design of solar window blinds, the flexible solar films are attached to one side of the window blinds, making use of the building facades. As solar films absorb the heat from sunlight, a significant decrease in energy conversion efficiency becomes one obstacle for widespread commercial application. In order to tackle the difficulty, this project yields an improvement, where a passive cooling coating (PCC) is applied to another side of the window blinds. The PCC makes the temperature of window blinds lower than the ambient temperature effectively, by emitting the long-wave infrared to the outer environment. With the aid of PCC, the lower in-room temperature is attained, resulting in less energy required for air conditioners during summers. The solar window blinds involve two work states: (I) solar films are orientated towards the sunlight to harvest energy; (II) PCCs are orientated towards the sunlight to cool down the surrounding temperature. The switch of work states between (I) and (II) is achieved by smart controllers based on temperature data acquired from sensors. A prototype is fabricated to demonstrated how much energy conversion efficiency is promoted with PCCs

Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original remote sensing image, could result in a collapse for the well-trained deep learning based SOD model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing SOD dataset (EORSSD) show the promising defense against adversarial cloud attacks

Joint metabolomic and transcriptomic analysis identify unique phenolic acid and flavonoid compounds associated with resistance to fusarium wilt in cucumber (Cucumis sativus L.)

IntroductionFusarium wilt (FW) caused by Fusarium oxysporum f. sp. cucumerinum (Foc) is a destructive soil-borne disease in cucumber (Cucumis sativus. L). However, there remains limited knowledge on the molecular mechanisms underlying FW resistance-mediated defense responses in cucumber.MethodsIn this study, metabolome and transcriptome profiling were carried out for two FW resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. NILs have shown consistent and stable resistance in multiple resistance tests conducted in the greenhouse and in the laboratory. A widely targeted metabolomic analysis identified differentially accumulated metabolites (DAMs) with significantly greater NR accumulation in response to Foc infection, including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway.ResultsTranscriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis and transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with the biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays. These assays found two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, as well as the flavonoid compound gallocatechin 3-O-gallate to have significant inhibitory effects on Foc growth. The antifungal effects of these three compounds represent a novel finding.DiscussionTherefore, phenolic acids and flavonoids play important roles in NR mediated FW resistance breeding in cucumber

Plant Defense Responses Induced by Two Herbivores and Consequences for Whitefly Bemisia tabaci

Diverse herbivores are known to induce various plant defenses. The plant defenses may detrimentally affect the performance and preference to subsequent herbivores on the same plant, such as affecting another insect’s feeding, settling, growth or oviposition. Here, we report two herbivores (mealybug Phenacoccus solenopsis and carmine spider mite Tetranychus cinnabarinus) which were used to pre-infest the cucumber to explore the impact on the plants and the later-colonizing species, whitefly Bemisia tabaci. The results showed that the whiteflies tended to select the treatments pre-infested by the mites, rather than the uninfected treatments. However, the result of treatments pre-infested by the mealybugs was opposite. Total number of eggs laid of whiteflies was related to their feeding preference. The results also showed that T. cinnabarinus were more likely to activate plant jasmonic acid (JA) regulated genes, while mealybugs were more likely to activate key genes regulated by salicylic acid (SA). The different plant defense activities on cucumbers may be one of the essential factors that affects the preference of B. tabaci. Moreover, the digestive enzymes and protective enzymes of the whitefly might play a substantial regulatory role in its settling and oviposition ability

EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV

Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object detectors, posing security concerns to the downstream tasks. However, the current public UAV datasets might ignore the diverse altitudes, vehicle attributes, fine-grained instance-level annotation in mostly side view with blurred vehicle roof, so none of them is good to study the adversarial patch based vehicle detection attack problem. In this paper, we propose a new dataset named EVD4UAV as an altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284 images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type), fine-grained annotation (horizontal and rotated bounding boxes, instance-level mask) in top view with clear vehicle roof. One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV. The experimental results show that these representative attack methods could not achieve the robust altitude-insensitive attack performance