Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Neuropeptide-Gated Perception of Appetitive Olfactory Inputs in Drosophila Larvae

SummaryUnderstanding how smell or taste translates into behavior remains challenging. We have developed a behavioral paradigm in Drosophila larvae to investigate reception and processing of appetitive olfactory inputs in higher-order olfactory centers. We found that the brief presentation of appetitive odors caused fed larvae to display impulsive feeding of sugar-rich food. Deficiencies in the signaling of neuropeptide F (NPF), the fly counterpart of neuropeptide Y (NPY), blocked appetitive odor-induced feeding by disrupting dopamine (DA)-mediated higher-order olfactory processing. We have identified a small number of appetitive odor-responsive dopaminergic neurons (DL2) whose activation mimics the behavioral effect of appetitive odor stimulation. Both NPF and DL2 neurons project to the secondary olfactory processing center; NPF and its receptor NPFR1 mediate a gating mechanism for reception of olfactory inputs in DL2 neurons. Our findings suggest that eating for reward value is an ancient behavior and that fly larvae are useful for studying neurobiology and the evolution of olfactory reward-driven behavior

Learning For Predictive Control: A Dual Gaussian Process Approach

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166

Study on Employee Satisfaction in Enterprises-- Based on the Empirical Analysis of Ningbo Foreign Trade Enterprises

By improving employee satisfaction and fully mobilize the enthusiasm of the employees improve the core competence of enterprises has become one of the important factors, this article through to ningbo home and foreign trade enterprise employee satisfaction survey, the empirical analysis of the influence factors of employee satisfaction, and puts forward relevant suggestions

StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation

Unconditional video generation is a challenging task that involves synthesizing high-quality videos that are both coherent and of extended duration. To address this challenge, researchers have used pretrained StyleGAN image generators for high-quality frame synthesis and focused on motion generator design. The motion generator is trained in an autoregressive manner using heavy 3D convolutional discriminators to ensure motion coherence during video generation. In this paper, we introduce a novel motion generator design that uses a learning-based inversion network for GAN. The encoder in our method captures rich and smooth priors from encoding images to latents, and given the latent of an initially generated frame as guidance, our method can generate smooth future latent by modulating the inversion encoder temporally. Our method enjoys the advantage of sparse training and naturally constrains the generation space of our motion generator with the inversion network guided by the initial frame, eliminating the need for heavy discriminators. Moreover, our method supports style transfer with simple fine-tuning when the encoder is paired with a pretrained StyleGAN generator. Extensive experiments conducted on various benchmarks demonstrate the superiority of our method in generating long and high-resolution videos with decent single-frame quality and temporal consistency.Comment: ICCV 2023. Code: https://github.com/johannwyh/StyleInV Project page: https://www.mmlab-ntu.com/project/styleinv/index.htm