Energy Mining, Earth’s Thermal Insulation Damaged and Trigger Climate Change

Fossil energy is the product of a series of complex chemical reactions inside the earth under high temperature and pressure. Where there is fossil energy, there must be a huge heat reservoir. The vast majority of coal, oil and gas are found in sedimentary basins with abundant geothermal resources. There is no “sea of oil” or “sea of gas” in the Earth’s crust. Oil, natural gas, shale gas, etc. exist underground in rock pores, cracks, caves, faults, sand grains where like a huge “capillary network”. Some cracks and faults reach deep into the entire crust. Oil, natural gas and shale gas seal off these pores, cracks, faults and sand layers, effectively preventing excessive leakage of heat from the ground. The enormous pressure of oil, gas and shale gas in the Earth’s crust counteracts the thermal pressure in the Earth’s interior, reaching a dynamic equilibrium. Once the oil, gas and shale gas is out of the ground, due to the loss of heat insulation and heat insulation material, the heat will eventually reach the surface from the Earth’s interior, causing the Earth’s crust “fever”. A large number of water vapor, carbon dioxide, methane, etc. Greenhouse gas from the crust into the atmosphere and ocean, destroyed the energy balance of the atmosphere. This article aims to find out the real causes of climate change. By collecting materials from published academic documents, it is clarified that the man-made damage to the Earth’s crust heat insulation seal is the truth of climate change. Therefore, the following conclusions are drawn: the thermal insulation of the Earth’s crust is damaged by mining fossil energy (coal, oil, natural gas, shale gas, oil shale, gas hydrate, etc.), too much heat from the Earth’s interior is pouring into the Earth’s surface, causing the Earth’s crust temperature and sea temperature to rise, trigger climate change and ecological disasters. Large amounts of water vapor have entered space, resulting rainfall and snow in some areas to exceed historical limits several times. Global soil and oceans degradation year by year

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design. The deliverables are released at https://laiyao1.github.io/maskplace

Lepton flavor violating μ→eγ\mu\to e\gamma and μ−e\mu-e conversion in unparticle physics

We have studied lepton flavor violation processes $\mu\to e\gamma$ and $\mu-e$ conversion in nuclei induced by unparticle. Both ${\rm Br}(\mu\to e\gamma)$ and $\mu-e$ conversion rate ${\rm CR}(\mu-e,{\rm Nuclei})$ strongly depend on the scale dimension $d_{\cal U}$ and the unparticle coupling $\lambda^{ff'}_{\rm K}$(K=V, A, S, P). Present experimental upper bounds on ${\rm Br}(\mu\to e\gamma)$, ${\rm CR}(\mu-e,{\rm Ti})$ and ${\rm CR}(\mu-e,{\rm Au})$ put stringent constraints on the parameters of unaprticle physics. The scale dimensions $d_{\cal U}$ around 2 are favored for the unparticle scale $\Lambda_{\cal U}$ of ${\cal O}(10 {\rm TeV})$ and the unparticle coupling of ${\cal O}(10^{-3})$. ${\rm CR}(\mu-e,{\rm Nuclei})$ is proportional to $\rm{Z^4_{eff}A^2/Z}$ for the pure vector and scalar couplings between unparticle and SM fermions, this peculiar atomatic number dependence can be used to distinguish unparticle from other theoretical models.Comment: 16 pages, 5 figure

EC^2: Emergent Communication for Embodied Control

Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.Comment: Published in CVPR202

Developing a Suitability Index for Residential Land Use: A case study in Dianchi Drainage Area

The conflict between residential land and agriculture land in China is increasingly sharpened, especially when some urban development began to sprawl to the suburban and rural areas. In order to plan land resources properly, land suitability assessment is often conducted to determine which type of land use is most appropriate for a particular location. The main objective of this study is to examine how land suitability assessment methods could be used in land planning processes in the Dianchi Drainage Area (DDA) in Southwest China to identify where future residential development should be located. The 1991 Toronto Waterfront Plan and the more recent 2005 Ontario Greenbelt Plan are examined and used to develop a framework which describes the potential for land suitability assessment in the DDA. Data limitations did not permit a suitability analysis to be completed for the DDA, however a description of methodologies for conducting residential land suitability analysis and required data are presented based on a review of relevant literature. The paper concludes with a discussion of the feasibility of land suitability in the DDA and other areas in China and also suggests opportunities for future research

Research on Personalized Recommender System for Tourism Information Service

Since the development in the 1990s, Recommender system has been widely applied in various fields. The conflict between the expansion of tourism information and difficulty of tourists obtaining tourism information allows Tourism Information Recommender System to have a practical significance. Based on the existing online tourism information service and the mature recommendation algorithms, Personal Recommender System can be used to solve present problems of the key recommendation algorithms. In the first place, this research presents an overview of researches on this issue both at home and abroad, and analyzes the applications of main stream recommendation algorithms. Secondly, a comparative study of domestic and international tourism information service websites is conducted. Drawbacks in their applications are defined and advantages are adopted in the settings of Recommender System. Finally, this research provides the framework of Recommender System, which combines the design and test of algorithms and the existing tourism information recommendation websites. This system allows customers to broaden experience of tourism information service and make tourism decisions more accurately and rapidly. Keywords: Tourism information service, Personalized recommendation, Intelligence recommendation module, Apriori algorith

Two-parameter estimation with three-mode NOON state in a symmetric three-well

We propose a theoretical scheme to realize two-parameter estimation via a Bose-Einstein condensates confined in a symmetric triple-well. The three-mode NOON state is prepared adiabatically as the initial state. Two phase differences between the wells are two parameters to be estimated. With the help of classical and quantum Fisher information, we study the sensitivity of the triple-well on estimating two phase parameters simultaneously. The result shows that the precision of simultaneous estimation of two parameters in a triple-well system can reach the Heisenberg scaling