Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion

The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (ɛ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of Nc. For the ɛ−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions. Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics schem

Stereo matching using higher-order graph cuts

Stereo matching is one of the fundamental tasks in early vision. Unlike human brain recognizes objects and estimates the depth easily, it is difficult to design algorithms that perform well on a computer due to variations of illumination, occlusion or textureless. Like most of the early vision problems, stereo matching can be formulated as an energy minimization problem in which the optimal depth is the one with the lowest energy. And graph cuts is one of the efficient and effective minimization tools that avoids the problems of local minima. Conventional energy functions are defined on Markov Random Fields (MRFs) with a 4-connected grid structure derived from the image, however it is incapable of expressing complex relationship between group of pixels. This thesis focuses on exploring some aspects of stereo matching problems through higher-order structure and higher-order graph cuts. The first problem I address relates to the evaluation of five state-of-the-art segmentation approaches. Their different contributions to segment-based stereo matching have been quantitatively measured and analyzed. This works aim at helping researchers to choose the segmentation approach that most suitable for their stereo matching application. The second part of the thesis proposes a novel approach to dense stereo matching. This method features sub-segmentation and adopts a higher-order potential to enforce the label consistency inside segments as a soft constraint. Moreover, several successful techniques have been combined. Experiments show that this approach obtains state-of-the-art results while still keeping efficiency. In the last part of the thesis, a novel two-layer MRFs framework is presented in which stereo matching and surface boundary estimation are combined. Both properties are inferred simultaneously and globally so that they can benefit each other. This work has direct application in phosphene vision based human indoor navigation. Experiments prove that the proposed framework achieves significantly better performance than other popular methods in all resolutions

Recycling of foundry waste materials

The recycling of a foundry ceramic waste from investment casting has been investigated. The waste was reduced in size by fly pressing and disc milling to d50 < 20 μm and cleaned by magnetic separation and acid leaching. The powder contained zircon, alumina and amorphous silica with 37, 38 and 24 wt. % (ZrSiO4: Al2O3: SiO2) respectively. Two products were targeted: zirconia toughened mullite (ZTM) ceramics produced with an addition of alumina and zircon based pigments developed by the removal of alumina and reaction with colourant ions. With an addition of 23.5 wt. % Al2O3, a ZTM containing 30 wt. % zirconia and 70 wt. % mullite exhibiting strength, hardness, thermal shock resistance and toughness commensurate with data reported in the literature were developed. Milling in isopropanol, dry pressing and sintering at 1600 ℃ for two hours optimised the properties. The transition to ZTM appeared to be through an intermediate glassy phase and limited by the dissociation of ZrSiO4. It was estimated that 70 % of the ZrO2 was transformable tetragonal without the addition of Y2O3. With Y2O3 non-transformable tetragonal ZrO2 was produced. It was shown that a clean zircon powder free of Al2O3 was generated by reaction with K2S2O7. Dissociation-synthesis and direct-synthesis routes were used to produce pigment. It was found that higher reaction temperature and the introduction of flux can significantly increase yellowness. The yellow produced from waste materials performed as well as those from commercial grade feeds

Role of microphysical parameterizations with droplet relative dispersion in IAP AGCM 4.1

Previous studies have shown that accurate descriptions of the cloud droplet effective radius (R (e)) and the autoconversion process of cloud droplets to raindrops (A (r)) can effectively improve simulated clouds and surface precipitation, and reduce the uncertainty of aerosol indirect effects in GCMs. In this paper, we implement cloud microphysical schemes including two-moment A (r) and R (e) considering relative dispersion of the cloud droplet size distribution into version 4.1 of the Institute of Atmospheric Physics&#39;s atmospheric GCM (IAP AGCM 4.1), which is the atmospheric component of the Chinese Academy of Sciences&#39; Earth System Model. Analysis of the effects of different schemes shows that the newly implemented schemes can improve both the simulated shortwave and longwave cloud radiative forcings, as compared to the standard scheme, in IAP AGCM 4.1. The new schemes also effectively enhance the large-scale precipitation, especially over low latitudes, although the influences of total precipitation are insignificant for different schemes. Further studies show that similar results can be found with the Community Atmosphere Model, version 5.1

Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding

We endow Large Language Models (LLMs) with fine-grained self-evaluation to refine multi-step reasoning inference. We propose an effective prompting approach that integrates self-evaluation guidance through stochastic beam search. Our approach explores the reasoning search space using a well-calibrated automatic criterion. This enables an efficient search to produce higher-quality final predictions. With the self-evaluation guided stochastic beam search, we also balance the quality-diversity trade-off in the generation of reasoning chains. This allows our approach to adapt well with majority voting and surpass the corresponding Codex-backboned baselines by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively, in few-shot accuracy. Analysis of our decompositional reasoning finds it pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://github.com/YuxiXie/SelfEval-Guided-Decoding.Comment: Our code is publicly available at https://github.com/YuxiXie/SelfEval-Guided-Decodin

PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network

IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vectorless IR analyses. Moreover, the proposed CNN model is general and transferable to different designs. This is in contrast to most existing machine learning (ML) approaches, where a model is applicable only to a specific design. Experimental results show that PowerNet outperforms the latest ML method by 9% in accuracy for the challenging case of vectorless IR drop and achieves a 30 times speedup compared to an accurate IR drop commercial tool. Further, a mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively, with very limited modification on their power grids

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.Comment: 6 pages, 2 figures, 5 tables, accepted by DAC'2