An Efficient FPGA-based Accelerator for Deep Forest

Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumption. In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA. Firstly, a delicate node computing unit (NCU) is designed to improve inference speed. Secondly, based on NCU, an efficient architecture and an adaptive dataflow are proposed, in order to alleviate the problem of node computing imbalance in the classification process. Moreover, an optimized storage scheme in this design also improves hardware utilization and power efficiency. The proposed design is implemented on an FPGA board, Intel Stratix V, and it is evaluated by two typical datasets, ADULT and Face Mask Detection. The experimental results show that the proposed design can achieve around 40x speedup compared to that on a 40 cores high performance x86 CPU.Comment: 5 pages, 5 figures, conferenc

Splitting of surface defect partition functions and integrable systems

We study Bethe/gauge correspondence at the special locus of Coulomb moduli where the integrable system exhibits the splitting of degenerate levels. For this investigation, we consider the four-dimensional pure $\mathcal{N}=2$ supersymmetric $U(N)$ gauge theory, with a half-BPS surface defect constructed with the help of an orbifold or a degenerate gauge vertex. We show that the non-perturbative Dyson-Schwinger equations imply the Schr\"odinger-type and the Baxter-type differential equations satisfied by the respective surface defect partition functions. At the special locus of Coulomb moduli the surface defect partition function splits into parts. We recover the Bethe/gauge dictionary for each summand.Comment: 34 pages, 2 figures; v2. published versio

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications

We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed, with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks, including image classification, instance and semantic segmentation, and notably, image generation. When integrated into generative models like U-Net in the latent diffusion model, DCNv4 outperforms its baseline, underscoring its possibility to enhance generative models. In practical applications, replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.Comment: Tech report; Code: https://github.com/OpenGVLab/DCNv

TaSnRK2.9, a Sucrose Non-fermenting 1-Related Protein Kinase Gene, Positively Regulates Plant Response to Drought and Salt Stress in Transgenic Tobacco

Sucrose non-fermenting 1-related protein kinase 2 (SnRK2) family members play crucial roles in plant abiotic stress response. However, the precise mechanism underlying the function of SnRKs has not been thoroughly elucidated in plants. In this research, a novel SnRK2 gene, TaSnRK2.9 was cloned and characterized from common wheat. The expression of TaSnRK2.9 was upregulated by polyethylene glycol (PEG), NaCl, H2O2, abscisic acid (ABA), methyl jasmonate (MeJA), and ethrel treatments. TaSnRK2.9 was mainly expressed in wheat young root, stamen, pistil, and lemma. Overexpressing TaSnRK2.9 in transgenic tobacco enhanced plants’ tolerance to drought and salt stresses both in young seedlings and mature plants with improved survival rate, seed germination rate, and root length. Physiological analyses suggest that TaSnRK2.9 improved antioxidant system such as superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and glutathione (GSH) to reduce the H2O2 content under drought or salt stress. Additionally, TaSnRK2.9 overexpression plants had elevated ABA content, implying that the function of TaSnRK2.9 may be ABA-dependent. Moreover, TaSnRK2.9 increased the expression of some ROS-related, ABA-related, and stress-response genes under osmotic or salt treatment. TaSnRK2.9 could interact with NtABF2 in yeast two-hybrid assay, and increased the expression of NtABF2 under mannitol or NaCl treatment in transgenic tobacco plants. In conclusion, overexpression of TaSnRK2.9 in tobacco conferred plants tolerance to drought and salt stresses through enhanced ROS scavenging ability, ABA-dependent signal transduction, and specific SnRK-ABF interaction

Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters

Continuous flow synthesis of N,O-dimethyl-N\u27-nitroisourea monitored by inline FTIR: comparing machine learning and kinetic modeling for optimization

The synthesis of N,O-Dimethyl-N\u27-nitroisourea, crucial intermediates in pesticide manufacturing, was explored through a substitution reaction between O-methyl-N-nitroisourea and methylamine within a novel continuous flow microreactor system, featuring FTIR inline analysis for real-time monitoring. This study embarked on a comparative analysis between two optimization approaches: the contemporary machine learning-based Bayesian optimization and the traditional kinetic modeling. Remarkably, both strategies obtained a similar yield of approximately 83 % under equivalent reaction parameters---specifically, an initial reactant concentration of 0.2 mol/L, a reaction temperature of 40 °C, a molar ratio of reactants at 5:1, and a residence time of 240 minutes. The Bayesian optimization method demonstrated a notable efficiency, achieving optimal conditions within a mere 20 experiments, in contrast to the kinetic modeling approach, which required a more laborious effort for model formulation and validation. Despite the long-standing reliance on kinetic modeling for its detailed insights into reaction dynamics, our findings suggest its relative inefficiency in optimization tasks compared to the machine learning-based alternative. This study not only highlights the potential of integrating advanced machine learning methods into chemical process optimization but also sets the stage for further exploration into efficient, data-driven approaches in chemical synthesis

Calculation of GTFP and its influencing factors in Guangdong-Hong Kong-Macao Greater Bay Area

Under the background of dual carbon target, the manufacturing industry in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is facing the pressure of green transformation. This paper takes this as the research basis, collects the production panel data of the manufacturing industry in the GBA in the past decade, calculates the green development level of the GBA and finds out the important influencing factors, so as to provide theoretical suggestions for the green production and green development of the manufacturing industry in the GBA

Laccase Biosensor Based on Electrospun Copper/Carbon Composite Nanofibers for Catechol Detection

The study compared the biosensing properties of laccase biosensors based on carbon nanofibers (CNFs) and copper/carbon composite nanofibers (Cu/CNFs). The two kinds of nanofibers were prepared by electrospinning and carbonization under the same conditions. Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Raman spectroscopy were employed to investigate the morphologies and structures of CNFs and Cu/CNFs. The amperometric results indicated that the Cu/CNFs/laccase(Lac)/Nafion/glass carbon electrode (GCE) possessed reliable analytical performance for the detection of catechol. The sensitivity of the Cu/CNFs/Lac/Nafion/GCE reached 33.1 μA/mM, larger than that of CNFs/Lac/Nafion/GCE. Meanwhile, Cu/CNFs/Lac/Nafion/GCE had a wider linear range from 9.95 × 10−6 to 9.76 × 10−3 M and a lower detection limit of 1.18 μM than CNFs/Lac/Nafion/GCE. Moreover, it exhibited a good repeatability, reproducibility, selectivity and long-term stability, revealing that electrospun Cu/CNFs have great potential in biosensing

The neural mechanism underlying illusory complex motions

PURPOSE: The Pinna illusion is a striking example of the perception of complex motion (rotation and radiation) in the absence of physical motion. For example, upon physically approaching or receding from the Pinna-Brelstaff figure, the observer experiences vivid illusory counter rotation of the figure. This visual phenomenon of illusory rotary motion is a well-known example of integration of local cues to form a global percept. Using psychophysical tests and functional magnetic resonance imaging (fMRI) of visual cortices, we recently found that the Pinna-Brelstaff figure (illusory rotation) and a real physical rotation control stimulus both predominantly activated subarea MST in hMT+, each with a similar response intensity. However, the detailed neural mechanisms underlying the Pinna illusory rotation as well as radiation remain unknown. METHODS: By manipulation of the physical characteristics of the Pinna-Brelstaff figure, we could generate different types of illusory complex motion: rotation, expansion and contraction (radiation). We first test the illusory effect in human and nonhuman primate psychophysically, and then performed single-unit recordings of MST and MT in awake macaques. RESULTS: We found that up to two-thirds of MST neurons encode Pinna illusory complex motions, with similar tuning preferences to their corresponding real physical motions. A subset of MT neurons was found to encode the local motion signals with earlier response latency than MST neurons. CONCLUSIONS: These results demonstrate that neurons in area MST but not MT respond equivalently and respectively to Pinna illusory and real rotations, expansions and contractions. These findings suggest that the representation of illusory and real complex motion fields in primate MST relies on a similar cascade of neural integrative mechanisms from earlier visual areas to generate a global motion perception.</p