A Novel Dynamic Light-Section 3D Reconstruction Method for Wide-Range Sensing.

Existing galvanometer-based laser-scanning systems are challenging to apply in multi-scale 3D reconstruction because of the difficulty in achieving a balance between a high reconstruction accuracy and a wide reconstruction range. This paper presents a novel method that synchronizes laser scanning by switching the field-of-view (FOV) of a camera using multi-galvanometers. Beyond the advanced hardware setup, we establish a comprehensive geometric model of the system by modeling dynamic camera, dynamic laser, and their combined interaction. Furthermore, since existing calibration methods mainly focus on either dynamic lasers or dynamic cameras and have certain limitations, we propose a novel high-precision and flexible calibration method by constructing an error model and minimizing the objective function. The performance of the proposed method was evaluated by scanning standard components. The results show that the proposed 3D reconstruction system achieves an accuracy of 0.3 mm when the measurement range is extended to 1100 mm × 1300 mm × 650 mm. This demonstrates that for meter-scale reconstruction ranges, a sub-millimeter measurement accuracy is achieved, indicating that the proposed method realizes multi-scale 3D reconstruction and simultaneously allows for high-precision and wide-range 3D reconstruction in industrial applications.

Plasmon-Induced Charge Transfer-Enhanced Raman Scattering on a Semiconductor: Toward Amplification-Free Quantification of SARS-CoV-2.

Semiconductors demonstrate great potentials as chemical mechanism-based surface-enhanced Raman scattering (SERS) substrates in determination of biological species in complex living systems with high selectivity. However, low sensitivity is the bottleneck for their practical applications, compared with that of noble metal-based Raman enhancement ascribed to electromagnetic mechanism. Herein, a novel Cu2 O nanoarray with free carrier density of 1.78×1021  cm-3 comparable to that of noble metals was self-assembled, creating a record in enhancement factor (EF) of 3.19×1010 among semiconductor substrates. The significant EF was mainly attributed to plasmon-induced hot electron transfer (PIHET) in semiconductor which was never reported before. This Cu2 O nanoarray was subsequently developed as a highly sensitive and selective SERS chip for non-enzyme and amplification-free SARS-CoV-2 RNA quantification with a detection limit down to 60 copies/mL within 5 min. This unique Cu2 O nanoarray demonstrated the significant Raman enhancement through PIHET process, enabling rapid and sensitive point-of-care testing of emerging virus variants.

Pillar[5]arene-Based Fluorescent Sensor Array for Biosensing of Intracellular Multi-neurotransmitters through Host-Guest Recognitions.

Neurotransmitters are very important for neuron events and brain diseases. However, effective probes for analyzing specific neurotransmitters are currently lacking. Herein, we design and create a supramolecular fluorescent probe (CN-DFP5) by synthesizing a dual-functionalized fluorescent pillar[5]arene derivative with borate naphthalene and aldehyde coumarin recognition groups to identify large-scale neurotransmitters. The developed probe can detect seven model neurotransmitters by generating different fluorescence patterns through three types of host-guest interactions. The obtained signals are statistically processed by principal component analysis, thus the high-throughput analysis of neurotransmitters is realized under dual-channel fluorescence responses. The present probe combines the advantages of small-molecule-based probes to easily enter into living neurons and cross-reactive sensor arrays. Thus, the selective binding enables this probe to identify specific neurotransmitters in biofluids, living neurons, and tissues. High selectivity and sensitivity further demonstrate that the molecular device could extend to more applications to detect and image neurotransmitters.

Encoding Morphogenesis of Quasi-Triangular Gold Nanoprisms with DNA.

Properties of gold nanoparticles vary with their morphologies. Typically, the research on the properties and applications of the nonequilibrium intermediates generated by the morphological evolution of triangular gold nanoprisms is still incomplete. Herein, we employ thiol-DNA (HS-DNA) to protect the low-stability quasi-nanoprisms with different truncation degrees (R values). The presence of HS-DNA not only increases the stability of the quasi-nanoprisms in different microenvironments, but also facilitates us to investigate their intrinsic plasmonic properties related to morphology. Additionally, we serve quasi-nanoprisms loaded with HS-DNA as assembly modules and nanoplatforms for programmable self-assembly higher-order hybrid structures, as well as carriers for encoding and decoding of orthogonal barcode-like information, which opens new opportunities for developing novel building blocks for light manipulation at nanoscale.

Ultrasensitive Sensing of Volatile Organic Compounds Using a Cu-Doped SnO2 -NiO p-n Heterostructure That Shows Significant Raman Enhancement*.

Surface enhanced Raman scattering (SERS) based on chemical mechanism (CM) attracts tremendous attention for great selectivity and stability. However, low enhancement factor (EF) limits its practical applications for trace detection. Here, a novel sponge-like Cu-doping SnO2 -NiO p-n semiconductor heterostructure (SnO2 -NiOx /Cu), was first created as a CM-based SERS substrate with a significant EF of 1.46×1010 . This remarkable EF was mainly attributed to the enhanced charge-separation efficacy of p-n heterojunction and charge transfer resonance resulted from Cu doping. Moreover, the porous structure enriched the probe molecules, resulting in further SERS signals magnification. By immobilizing CuPc as an inner-reference element, SnO2 -NiOx /Cu was developed as a SERS nose for selective recognition of multiple lung cancer related VOCs down to ppb level. The information of VOCs was recorded in a barcode, demonstrating practical potential of a desktop SERS device for biomarker screening.

AIE-Active Chiral [3]Rotaxanes with Switchable Circularly Polarized Luminescence.

The construction of circularly polarized luminescence (CPL) switches with multiple switchable emission states and high dissymmetry factors (glum ) has attracted increasing attention due to their broad applications in diverse fields such as the development of smart devices and sensors. Herein, a new family of AIE-active chiral [3]rotaxanes were designed and synthesized, from which a novel CPL switching system was successfully constructed. The switching process was realized through the controlled motions of the chiral pillar[5]arene macrocycles along the axle through the addition or removal of the acetate anions, which not only modulated the chirality information transfer but also tuned the aggregations of the integrated [3]rotaxanes, thus resulting in reversible transformations between two emission states with both high photoluminescence quantum yields (PLQYs) and high dissymmetry factors (glum ) values.

An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion.

An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames'. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.

Direct Amination of Alcohols Catalyzed by Aluminum Triflate: An Experimental and Computational Study.

Among the best-performing homogeneous catalysts for the direct amination of activated secondary alcohols with electron-poor amine derivatives, metal triflates, such as aluminum triflate, Al(OTf)3 , stand out. Herein we report the extension of this reaction to electron-rich amines and activated primary alcohols. We provide detailed insight into the structure and reactivity of the catalyst under working conditions in both nitromethane and toluene solvent, through experiment (cyclic voltammetry, conductimetry, NMR spectroscopy), and density functional theory (DFT) simulations. Competition between aniline and benzyl alcohol for Al in the two solvents explains the different reactivities. The catalyst structures predicted from the DFT calculations were validated by the experiments. Whereas a SN 1-type mechanism was found to be active in nitromethane, we propose a SN 2 mechanism in toluene to rationalize the much higher selectivity observed when using this solvent. Also, unlike what is commonly assumed in homogeneous catalysis, we show that different active species may be active instead of only one.

Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror.

This study proposes a novel concept of actuator-driven frame-by-frame intermittent tracking for motion-blur-free video shooting of fast-moving objects. The camera frame and shutter timings are controlled for motion blur reduction in synchronization with a free-vibration-type actuator vibrating with a large amplitude at hundreds of hertz so that motion blur can be significantly reduced in free-viewpoint high-frame-rate video shooting for fast-moving objects by deriving the maximum performance of the actuator. We develop a prototype of a motion-blur-free video shooting system by implementing our frame-by-frame intermittent tracking algorithm on a high-speed video camera system with a resonant mirror vibrating at 750 Hz. It can capture 1024 × 1024 images of fast-moving objects at 750 fps with an exposure time of 0.33 ms without motion blur. Several experimental results for fast-moving objects verify that our proposed method can reduce image degradation from motion blur without decreasing the camera exposure time.

PWSNAS: Powering Weight Sharing NAS With General Search Space Shrinking Framework.

Neural architecture search (NAS) depends heavily on an efficient and accurate performance estimator. To speed up the evaluation process, recent advances, like differentiable architecture search (DARTS) and One-Shot approaches, instead of training every model from scratch, train a weight-sharing super-network to reuse parameters among different candidates, in which all child models can be efficiently evaluated. Though these methods significantly boost search efficiency, they inherently suffer from inaccurate and unstable performance estimation. To this end, we propose a general and effective framework for powering weight-sharing NAS, namely, PWSNAS, by shrinking search space automatically, i.e., candidate operators will be discarded if they are less important. With the strategy, our approach can provide a promising search space of a smaller size by progressively simplifying the original search space, which can reduce difficulties for existing NAS methods to find superior architectures. In particular, we present two strategies to guide the shrinking process: detect redundant operators with a new angle-based metric and decrease the degree of weight sharing of a super-network by increasing parameters, which differentiates PWSNAS from existing shrinking methods. Comprehensive analysis experiments on NASBench-201 verify the superiority of our proposed metric over existing accuracy-based and magnitude-based metrics. PWSNAS can easily apply to the state-of-the-art NAS methods, e.g., single path one-shot neural architecture search (SPOS), FairNAS, ProxylessNAS, DARTS, and progressive DARTS (PDARTS). We evaluate PWSNAS and demonstrate consistent performance gains over baseline methods.

PSAQ-ViT V2: Toward Accurate and General Data-Free Quantization for Vision Transformers.

Data-free quantization can potentially address data privacy and security concerns in model compression and thus has been widely investigated. Recently, patch similarity aware data-free quantization for vision transformers (PSAQ-ViT) designs a relative value metric, patch similarity, to generate data from pretrained vision transformers (ViTs), achieving the first attempt at data-free quantization for ViTs. In this article, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT. More specifically, following the patch similarity metric in PSAQ-ViT, we introduce an adaptive teacher-student strategy, which facilitates the constant cyclic evolution of the generated samples and the quantized model in a competitive and interactive fashion under the supervision of the full-precision (FP) model (teacher), thus significantly improving the accuracy of the quantized model. Moreover, without the auxiliary category guidance, we employ the task-and model-independent prior information, making the general-purpose scheme compatible with a broad range of vision tasks and models. Extensive experiments are conducted on various models on image classification, object detection, and semantic segmentation tasks, and PSAQ-ViT V2, with the naive quantization strategy and without access to real-world data, consistently achieves competitive results, showing potential as a powerful baseline on data-free quantization for ViTs. For instance, with Swin-S as the (backbone) model, 8-bit quantization reaches 82.13 top-1 accuracy on ImageNet, 50.9 box AP and 44.1 mask AP on COCO, and 47.2 mean Intersection over Union (mIoU) on ADE20K. We hope that accurate and general PSAQ-ViT V2 can serve as a potential and practice solution in real-world applications involving sensitive data. Code is released and merged at: https://github.com/zkkli/PSAQ-ViT.

The complete chloroplast genome of Vaccinium oxycoccos (Ericaceae).

Vaccinium species have great significance as fruit crops due to their economic and food values. Here we report the chloroplast genome of V. oxycoccos. The chloroplast genome of V. oxycoccos was 177,088 bp in length with a GC content of 36.74%. LSC, SSC, and IR regions were 104,139 bp, 3031 bp, and 34,959 bp in length, respectively. The chloroplast genome contained 105 different genes, including 73 protein-coding genes, 4 rRNA genes, and 28 tRNA genes. The phylogenetic analysis indicated that V. oxycoccos was closely related to V. microcarpum in the family Ericaceae. This chloroplast genome not only enriches the genome information of Vaccinium, but also will be useful in the evolution study of the family Ericaceae.

Dynamic Changes in the Global Transcriptome of Postnatal Skeletal Muscle in Different Sheep.

Sheep growth performance, mainly skeletal muscle growth, provides direct economic benefits to the animal husbandry industry. However, the underlying genetic mechanisms of different breeds remain unclear. We found that the cross-sectional area (CSA) of skeletal muscle in Dorper (D) and binary cross-breeding (HD) was higher than that in Hu sheep (H) from 3 months to 12 months after birth. The transcriptomic analysis of 42 quadriceps femoris samples showed that a total of 5053 differential expression genes (DEGs) were identified. The differences in the global gene expression patterns, the dynamic transcriptome of skeletal muscle development, and the transcriptome of the transformation of fast and slow muscles were explored using weighted correlation network analysis (WGCNA) and allele-specific expression analysis. Moreover, the gene expression patterns of HD were more similar to D rather than H from 3 months to 12 months, which might be the reason for the difference in muscle growth in the three breeds. Additionally, several genes (GNB2L1, RPL15, DVL1, FBXO31, etc.) were identified as candidates related to skeletal muscle growth. These results should serve as an important resource revealing the molecular basis of muscle growth and development in sheep.

Genome-wide epigenetic dynamics during postnatal skeletal muscle growth in Hu sheep.

Hypertrophy and fiber transformation are two prominent features of postnatal skeletal muscle development. However, the role of epigenetic modifications is less understood. ATAC-seq, whole genome bisulfite sequencing, and RNA-seq were applied to investigate the epigenetic dynamics of muscle in Hu sheep at 3 days, 3 months, 6 months, and 12 months after birth. All 6865 differentially expressed genes were assigned into three distinct tendencies, highlighting the balanced protein synthesis, accumulated immune activities, and restrained cell division in postnatal development. We identified 3742 differentially accessible regions and 11799 differentially methylated regions that were associated with muscle-development-related pathways in certain stages, like D3-M6. Transcription factor network analysis, based on genomic loci with high chromatin accessibility and low methylation, showed that ARID5B, MYOG, and ENO1 were associated with muscle hypertrophy, while NR1D1, FADS1, ZFP36L2, and SLC25A1 were associated with muscle fiber transformation. Taken together, these results suggest that DNA methylation and chromatin accessibility contributed toward regulating the growth and fiber transformation of postnatal skeletal muscle in Hu sheep.

Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud.

We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches.