K moduli of log del Pezzo pairs

We establish the full explicit wall-crossing for K-moduli space $\overline{P}^K_c$ of degree $8$ del Pezzo pairs $(X,cC)$ where generically X \cong \bbF_1 and $C \sim -2K_X$. We also show K-moduli spaces $\overline{P}^K_c$ coincide with Hassett-Keel-Looijenga(HKL) models \cF(s) of a $18$-dimensional locally symmetric spaces associated to the lattice $E_8\oplus U^2\oplus E_7\oplus A_1$.Comment: 42 pages, comments welcome

Birational geometry of moduli space of del Pezzo pairs

In this paper, we investigate the geometry of moduli space $P_d$ of degree $d$ del Pezzo pair, that is, a del Pezzo surface $X$ of degree $d$ with a curve $C \sim -2K_X$. More precisely, we study compactifications for $P_d$ from both Hodge's theoretical and geometric invariant theoretical (GIT) perspective. We compute the Picard numbers of these compact moduli spaces which is an important step to set up the Hassett-Keel-Looijenga models for $P_d$. For $d=8$ case, we propose the Hassett-Keel-Looijenga program \cF_8(s)=\Proj(R(\cF_8,\Delta(s) ) as the section rings of certain \bQ-line bundle $\Delta_8(s)$ on locally symmetric variety \cF_8, which is birational to $P_8$. Moreover, we give an arithmetic stratification on \cF_8. After using the arithmetic computation of pullback $\Delta(s)$ on these arithmetic strata, we give the arithmetic predictions for the wall-crossing behavior of \cF_8(s) when $s\in [0,1]$ varies. The relation of \cF_8(s) with the K-moduli spaces of degree $8$ del Pezzo pairs is also proposed.Comment: 43 pages, comments are very welcome

The Application Of The IoT For Minimizing Consumption In Smart Home

Excessive consumption leads to 7 trends of crises, including destruction of the atmosphere, energy crisis, social decline and conflicts. Over consumption also deteriorates human health. To reduce excessive consumption not only can improve health, it can also secure home safety and less energy consumption. The reducing over consumption can benefit human health and environmental protection. This motivates us to devise an innovative smart home App (SHA). After a survey to potential users, it reveals that the new features can help reduce the excessive consumption and deterioration of the human health as well as the transportation, healthcare and destruction of earth environment. Enterprises can also achieve their social responsibility through the implementation and popularization of the SHA as soon as possible

Portable Intelligent Oscilloscope Based on Innovative Education

Based on the innovative education idea that students in various universities can do experiments anytime and anywhere without being limited by the course time, a portable oscilloscope suitable for students' experiment and teaching practice is designed by using Arduino, Android and Bluetooth Technology. This oscilloscope not only realizes the basic functions of an oscilloscope, but also makes the measurement images of low-frequency signals more clear and impressive. In addition, the design based on the mobile App is more user-friendly, which enhances the user's sense of use and facilitates the sorting and query of experimental data. The application test shows that the oscilloscope has stable performance, clear waveform, satisfies students' learning and teaching practice to a large extent, and has a good development prospect

Intelligent Modeling Approach to Predict Effluent Quality of Wastewater Treatment Process

Monitoring of effluent quality remains a challenge to the wastewater treatment process (WWTP). In order to provide a reliable tool for the online monitoring of effluent quality, an intelligent modeling approach, which consists of online sensors and an effluent quality predicting plant, is developed to predict effluent quality in this chapter. The intelligent modeling approach, based on a self-organizing fuzzy neural network (SOFNN), is able to enhance the modeling performance by organizing the structure and adjusting the parameters simultaneously. The experimental studies of intelligent modeling approach have been performed on several systems to verify the effectiveness. The comparison with other existing methods has been made and demonstrated that the intelligent modeling approach is of better performance

Steering of magnetotactic bacterial microrobots by focusing magnetic field for targeted pathogen killing

International audienceTargeted steering of magnetotactic bacterial microrobots is a growing tendency for their various biomedical applications. However, real-time monitoring during their movements and targeted cell killing in specific locations remains challenging. Here, we steered bacterial microrobots to target and attach to Staphylococcus aureus that was subsequently killed in a magnetic target device, which can realize guiding, mixing, and killing for targeted therapy. The generated focusing magnetic field was applied to magnetotactic bacterial microrobots, and the realizability of control strategies was analyzed. We successfully guided magnetotactic bacterial microrobots in microfluidic chips without real-time monitoring of their location. After mixing with microrobots under a rotating magnetic field for their attachment, the pathogen was killed under a swinging magnetic field. These results suggest that targeted therapy with these microrobots by using a magnetic target device is a promising approach

MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation

Long-tailed distribution of semantic categories, which has been often ignored in conventional methods, causes unsatisfactory performance in semantic segmentation on tail categories. In this paper, we focus on the problem of long-tailed semantic segmentation. Although some long-tailed recognition methods (e.g., re-sampling/re-weighting) have been proposed in other problems, they can probably compromise crucial contextual information and are thus hardly adaptable to the problem of long-tailed semantic segmentation. To address this issue, we propose MEDOE, a novel framework for long-tailed semantic segmentation via contextual information ensemble-and-grouping. The proposed two-sage framework comprises a multi-expert decoder (MED) and a multi-expert output ensemble (MOE). Specifically, the MED includes several "experts". Based on the pixel frequency distribution, each expert takes the dataset masked according to the specific categories as input and generates contextual information self-adaptively for classification; The MOE adopts learnable decision weights for the ensemble of the experts' outputs. As a model-agnostic framework, our MEDOE can be flexibly and efficiently coupled with various popular deep neural networks (e.g., DeepLabv3+, OCRNet, and PSPNet) to improve their performance in long-tailed semantic segmentation. Experimental results show that the proposed framework outperforms the current methods on both Cityscapes and ADE20K datasets by up to 1.78% in mIoU and 5.89% in mAcc.Comment: 18 pages, 9 figure