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The past decade has demonstrated the potential of human activity recognition (HAR) with WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely concentrated on enhancing precision through sophisticated models. However, the complexity of recognition tasks has been largely neglected. Thus, the performance of the HAR system is markedly diminished when tasked with increasing complexities, such as a larger classification number, the confusion of similar actions, and signal distortion To address this issue, we eliminated conventional convolutional and recurrent backbones and proposed WiTransformer, a novel tactic based on pure Transformers. Nevertheless, Transformer-like models are typically suited to large-scale datasets as pretraining models, according to the experience of the Vision Transformer. Therefore, we adopted the Body-coordinate Velocity Profile, a cross-domain WiFi signal feature derived from the channel state information, to reduce the threshold of the Transformers. Based on this, we propose two modified transformer architectures, united spatiotemporal Transformer (UST) and separated spatiotemporal Transformer (SST) to realize WiFi-based human gesture recognition models with task robustness. SST intuitively extracts spatial and temporal data features using two encoders, respectively. By contrast, UST can extract the same three-dimensional features with only a one-dimensional encoder, owing to its well-designed structure. We evaluated SST and UST on four designed task datasets (TDSs) with varying task complexities. The experimental results demonstrate that UST has achieved recognition accuracy of 86.16% on the most complex task dataset TDSs-22, outperforming the other popular backbones. Simultaneously, the accuracy decreases by at most 3.18% when the task complexity increases from TDSs-6 to TDSs-22, which is 0.14–0.2 times that of others. However, as predicted and analyzed, SST fails because of excessive lack of inductive bias and the limited scale of the training data. [ABSTRACT FROM AUTHOR]

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV's status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset. [ABSTRACT FROM AUTHOR]

Two new phthalide derivatives (1–2) and four known phthalide compounds (3–6) were purified from the culture of a mangrove endophytic fungus Pestalotiopsis sp. SAS4. Their chemical structures were established by analyses of 1D and 2D nuclear magnetic resonance (NMR) and high resolution mass spectrometry (HR‐MS) spectroscopic data. All of these compounds were evaluated in vitro for antibacterial, cytotoxicity, and resistance to hypoxic–ischemic injury activities. [ABSTRACT FROM AUTHOR]

We have confirmed the presence of narrow, degenerately-doped axial silicon nanowire (SiNW) p-n junctions via off-axis electron holography (EH). SiNWs were grown via the vapor-solid-liquid (VLS) mechanism using gold (Au) as the catalyst, silane (SiH4), diborane (B2H6) and phosphine (PH3) as the precursors, and hydrochloric acid (HCl) to stabilize the growth. Two types of growth were carried out, and in each case we explored growth with both n/p and p/n sequences. In the first type, we abruptly switched the dopant precursors at the desired junction location, and in the second type we slowed the growth rate at the junction to allow the dopants to readily leave the Au catalyst. We demonstrate degenerately-doped p/n and n/p nanowire segments with abrupt potential profiles of 1.02 ± 0.02 and 0.86 ± 0.3 V, and depletion region widths as narrow as 10 ± 1 nm via EH. Low temperature current-voltage measurements show an asymmetric curvature in the forward direction that resemble planar gold-doped tunnel junctions, where the tunneling current is hidden by a large excess current. The results presented herein show that the direct VLS growth of degenerately-doped axial SiNW p-n junctions is feasible, an essential step in the fabrication of more complex SiNW-based devices for electronics and solar energy. [ABSTRACT FROM AUTHOR]

The precursor of poly(carborane)(silicon-acetylene) (PCBS) for silicon carbide (SiC)/B4C composite ceramics was synthesized by a salt metathesis reaction. The molecular weight of PCBS was measured by gel permeation chromatography and the corresponding structures were identified by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR). After thermal curing, the PCBS was converted to a dark red brittle nonporous monolith and the thermal stability was significantly improved, especially in an air atmosphere. Thermogravimetric analysis indicated that the 5% weight loss temperatures were 595°C and 633°C under nitrogen and air atmospheres, respectively, and the corresponding residues at 1000°C were 86.42% and 90.42%. After a heat treatment at 1600°C under inert atmosphere, the well-dispersed SiC/B4C ceramic was obtained and characterized by FTIR, X-ray diffraction analysis, Raman spectroscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The obtained SiC/B4C ceramic has a good high-temperature and oxidation resistance which can be applied to aviation, aerospace, and other ultra-high temperature conditions. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum) serves as a research model for fruit development; however, while it is an important dietary source of antioxidant nutrients, the transcriptional regulation of genes that determine nutrient levels remains poorly understood. Here, the transcriptomes of fruit at seven developmental stages (7, 14, 21, 28, 35, 42 and 49 days after flowering) from two tomato cultivars (Ailsa Craig and HG6-61) were evaluated using the Illumina sequencing platform. A total of 26,397 genes, which were expressed in at least one developmental stage, were detected in the two cultivars, and the expression patterns of those genes could be divided into 20 groups using a K-mean cluster analysis. Gene Ontology term enrichment analysis indicated that genes involved in RNA regulation, secondary metabolism, hormone metabolism and cell wall metabolism were the most highly differentially expressed genes during fruit development and ripening. A co-expression analysis revealed several transcription factors whose expression patterns correlated with those of genes associated with ascorbic acid, carotenoid and flavonoid biosynthesis. This transcriptional correlation was confirmed by agroinfiltration mediated transient expression, which showed that most of the enzymatic genes in the ascorbic acid biosynthesis were regulated by the overexpression of each of the three transcription factors that were tested. The metabolic dynamics of ascorbic acid, carotenoid and flavonoid were investigated during fruit development and ripening, and some selected transcription factors showed transcriptional correlation with the accumulation of ascorbic acid, carotenoid and flavonoid. This transcriptome study provides insight into the regulatory mechanism of fruit development and presents candidate transcription factors involved in secondary metabolism. [ABSTRACT FROM AUTHOR]