High-fidelity 3D Reconstruction of Plants using Neural Radiance Field

Accurate reconstruction of plant phenotypes plays a key role in optimising sustainable farming practices in the field of Precision Agriculture (PA). Currently, optical sensor-based approaches dominate the field, but the need for high-fidelity 3D reconstruction of crops and plants in unstructured agricultural environments remains challenging. Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields. This technique has shown impressive performance in various novel vision synthesis tasks, but has remained relatively unexplored in the agricultural context. In our study, we focus on two fundamental tasks within plant phenotyping: (1) the synthesis of 2D novel-view images and (2) the 3D reconstruction of crop and plant models. We explore the world of neural radiance fields, in particular two SOTA methods: Instant-NGP, which excels in generating high-quality images with impressive training and inference speed, and Instant-NSR, which improves the reconstructed geometry by incorporating the Signed Distance Function (SDF) during training. In particular, we present a novel plant phenotype dataset comprising real plant images from production environments. This dataset is a first-of-its-kind initiative aimed at comprehensively exploring the advantages and limitations of NeRF in agricultural contexts. Our experimental results show that NeRF demonstrates commendable performance in the synthesis of novel-view images and is able to achieve reconstruction results that are competitive with Reality Capture, a leading commercial software for 3D Multi-View Stereo (MVS)-based reconstruction. However, our study also highlights certain drawbacks of NeRF, including relatively slow training speeds, performance limitations in cases of insufficient sampling, and challenges in obtaining geometry quality in complex setups

Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields

Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyping of plants in farm environments. This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments. To quantitatively evaluate the performance of this method, traditional point cloud registration on 3D scanning data is implemented for comparison. Experimental result shows that NeRF(Neural Radiance Fields) achieves competitive accuracy compared to the 3D scanning methods. The mean distance error between the scanner-based method and the NeRF-based method is 0.865mm. This study shows that the learning-based NeRF method achieves similar accuracy to 3D scanning-based methods but with improved scalability and robustness

Observation of counterflow superfluidity in a two-component Mott insulator

The counterflow superfluidity (CSF) was predicted two decades ago. Counterintuitively, while both components in the CSF have fluidity, their correlated counterflow currents cancel out leading the overall system to an incompressible Mott insulator. However, realizing and identifying the CSF remain challenging due to the request on extreme experimental capabilities in a single setup. Here, we observe the CSF in a binary Bose mixture in optical lattices. We prepare a low-entropy spin-Mott state by conveying and merging two spin-1/2 bosonic atoms at every site and drive it adiabatically to the CSF at $\sim$ 1 nK. Antipair correlations of the CSF are probed though a site- and spin-resolved quantum gas microscope in both real and momentum spaces. These techniques and observations provide accessibility to the symmetry-protected topological quantum matters.Comment: 13 pages, 10 figure

Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.Comment: 13 pages

Effect of piston-slipper assembly mass difference on the cylinder block tilt in a high-speed electro-hydrostatic actuator pump of aircraft

When manufacturing axial piston pumps, mass difference of piston-slipper assembly is inevitable because of manufacturing precision limits. Small mass difference may not cause problems when the pump operates at low speeds, while it cannot be ignored at high speeds. One problem related to high speed is the cylinder block tilt resulting from the inertial effect of piston-slipper assembly. Recently, the speed of electro-hydrostatic actuator (EHA) pump in aircraft can reach more than 10,000 rpm. Therefore, mass difference of pistonslipper assembly should be taken into account in future EHA pump design. The main purpose of this paper is to investigate the effect of the mass difference of piston-slipper assembly on the cylinder block tilt in a high-speed EHA pump. A detailed set of relevant equations is developed to establish the relationship between the mass difference of piston-slipper assembly and cylinder block tilting moment. It is found that a tighter control over the mass difference of piston-slipper assembly should be guaranteed when it comes to high-speed EHA pumps

Multiphoton graph states from a solid-state single-photon source

This work was supported by the National Natural Science Foundation of China (Grants No. 11575174, No. 11674308, No. 11704424, and No. 11774326), the Chinese Academy of Sciences, and the National Key Research and Development Program of China.Photonic graph states are underlying resources for one-way optical quantum computation, quantum error correction, fundamental testing of quantum mechanics, and quantum communication networks. Most existing works, however, are based on the spontaneous parametric down-conversion sources that intrinsically suffer from probabilistic generation and double pair components. Here, we create two important classes of graph states, a polarization-encoded four-photon Greenberger–Horne–Zeilinger (GHZ) state and a linear cluster state, by actively demultiplexing a deterministic single-photon source from a semiconductor quantum dot embedded in a micropillar. A state fidelity of 0.790 ± 0.009 (0.763 ± 0.004) and a count rate of ∼13 Hz are observed for the four-photon GHZ (cluster) state. The results constitute a new route toward the multiphoton entanglement with deterministic single-photon sources.PostprintPeer reviewe

Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor

Adiabatic quantum computing enables the preparation of many-body ground states. This is key for applications in chemistry, materials science, and beyond. Realisation poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitised version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimisation of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multi-qubit superconducting coprocessor. We track the real-time evolution of the ground and exited states of transverse-field Ising spins with a fidelity up that can reach about 99%.Comment: 12 pages, 4 figure

Proof-of-principle demonstration of compiled Shor's algorithm using a quantum dot single-photon source

Funding: National Natural Science Foundation of China (11575174, 11674308, 11704424, 11774326, 11874346); Chinese Academy of Sciences; National Key Research and Development Program of China.We report a proof-of-principle demonstration of Shor’s algorithm with photons generated by an on-demand semiconductor quantum dot single-photon source for the first time. A fully compiled version of Shor’s algorithm for factoring 15 has been accomplished with a significantly reduced resource requirement that employs the four-photon cluster state. Genuine multiparticle entanglement properties are confirmed to reveal the quantum character of the algorithm and circuit. The implementation realizes the Shor’s algorithm with deterministic photonic qubits, which opens new applications for cluster state beyond one-way quantum computing.Publisher PDFPeer reviewe

A superior preparing method for daidzein-hydroxypropyl-β-cyclodextrin complexes with improved solubility and dissolution: supercritical fluid process

Advantages of the supercritical fluid (SCF) process compared to the conventional solution stirring method (CSSM) in the preparation of daidzein-hydroxypropyl-β-cyclodextrin (HPβCD) complexes were investigated. Formation of daidzein/HPβCD inclusion complexes was confirmed by Fourier transformed-infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and scanning electron microscopy (SEM). Particle size, inclusion yield, drug solubility and dissolution of daidzein/HPβCD complexes were evaluated. Compared to CSSM, the SCF process resulted in higher inclusion yield and higher solubility. Also, extended dissolution of daidzein from the SCF processed HPβCD inclusion complexes was observed, with only 22.94 % released in 45 min, compared to its rapid release from those prepared by CSSM, with 98.25 % drug release in 15 min. This extended release of daidzein from SCF prepared inclusion complexes was necessary to avoid drug precipitation and improve drug solubilisation in the gastrointestinal tract. The results showed that the SCF process is a superior preparation method for daidzein-hydroxypropyl-β-cyclodextrin complexes