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关键词： Fossil fuels   Computational science   Computer science  

摘要： Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver;if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information;whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

关键词： Computer science   Quantum information   Statistical physics  

摘要： Quantum computers promise to solve certain computational problems much faster than classical computers. However, current quantum processors are limited by their modest size and appreciable error rates. Recent efforts to demonstrate quantum speedups have therefore focused on problems that are both classically hard and naturally suited to current quantum hardware, such as sampling from complicated-although not explicitly useful-probability distributions(1-3). Here we introduce and experimentally demonstrate a quantum algorithm that is similarly well suited to current hardware, but which samples from complicated distributions arising in several applications. The algorithm performs Markov chain Monte Carlo (MCMC), a prominent iterative technique(4), to sample from the Boltzmann distribution of classical Ising models. Unlike most near-term quantum algorithms, ours provably converges to the correct distribution, despite being hard to simulate classically. But like most MCMC algorithms, its convergence rate is difficult to establish theoretically, so we instead analysed it through both experiments and simulations. In experiments, our quantum algorithm converged in fewer iterations than common classical MCMC alternatives, suggesting unusual robustness to noise. In simulations, we observed a polynomial speedup between cubic and quartic over such alternatives. This empirical speedup, should it persist to larger scales, could ease computational bottlenecks posed by this sampling problem in machine learning(5), statistical physics(6) and optimization(7). This algorithm therefore opens a new path for quantum computers to solve useful-not merely difficult-sampling problems.

关键词： China   environmental destruction   flash ethnography   wild rivers  

摘要： This flash piece asks what development means for rivers, indigenous farmers, and tourists when we redirect, move, and siphon water from one region to another. The work considers rivers as traces of time, peoples, and environments, following my chronology and physical residence(s) in California, Hunan, Guangxi, and Yunnan in China. I begin in the Bay Area, where I grew up, and move to my time spent on five rivers in Southern China: the Xiangjiang, the Li, the Jinsha, the Lancang, and the Nujiang.

关键词： Students  

摘要： The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the same teacher at a difference of two weeks between lectures). We tried to minimize external factors’ impact by carefully selecting the dates (close ones), having the courses in the same classroom and on the same day of the week, at the same hour, and checking the number and the complexity of the introduced items to be comparable. We asked for students’ feedback and we define a set of countable body signs for their involvement in the course. The results were comparable by both metrics (body language) and text analysis results, students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions. © 2023, CC BY-NC-ND.

摘要： By developing in vitro 3D culture systems and identifying morphological and molecular events of early organogenesis, two recent studies1,2 reported exciting research advances in non-human primate embryo development.

关键词： Computational chemistry   Computer science  

摘要： Crystalline materials enable essential technologies, and their properties are determined by their structures. Crystal structure prediction can thus play a central part in the design of new functional materials(1,2). Researchers have developed efficient heuristics to identify structural minima on the potential energy surface(3-5). Although these methods can often access all configurations in principle, there is no guarantee that the lowest energy structure has been found. Here we show that the structure of a crystalline material can be predicted with energy guarantees by an algorithm that finds all the unknown atomic positions within a unit cell by combining combinatorial and continuous optimization. We encode the combinatorial task of finding the lowest energy periodic allocation of all atoms on a lattice as a mathematical optimization problem of integer programming(6,7), enabling guaranteed identification of the global optimum using well-developed algorithms. A single subsequent local minimization of the resulting atom allocations then reaches the correct structures of key inorganic materials directly, proving their energetic optimality under clear assumptions. This formulation of crystal structure prediction establishes a connection to the theory of algorithms and provides the absolute energetic status of observed or predicted materials. It provides the ground truth for heuristic or data-driven structure prediction methods and is uniquely suitable for quantum annealers(8-10), opening a path to overcome the combinatorial explosion of atomic configurations.

关键词： Computer science   Quantum information  

摘要： Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions. Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

关键词： 乳腺癌   三位一体互动护理模式   心理健康   生活质量  

摘要： 目的探讨三位一体互动护理模式在乳腺癌患者中的应用效果。方法选择2020年1月至2022年4月治疗的乳腺癌患者124例,按照随机数表法分为对照组和观察组,每组62例,均为女性。对照组应用常规护理模式,观察组采用三位一体互动护理模式,比较2组并发症发生情况、SAS评分、SDS评分、生活质量评分以及睡眠质量评分。结果对照组肺部感染、肝肾毒性、恶心呕吐、骨髓抑制的相关并发症明显高于观察组,差异有统计学意义(P<0.05)。对照组睡眠质量评分、负性情绪评分高于对照组,差异有统计学意义(P<0.05)。对照组各维度生活质量评分均低于观察组,差异有统计学意义(P<0.05)。结论采用新的互动护理模式,使乳腺癌患者的生活质量、睡眠质量等得以改善,在减少化疗不良反应的同时利于身心健康状态的恢复。

关键词： 学科建设   人才培养   教学意识培养   制度   监控体系   教育信息化  

摘要： 信息技术的快速发展和在各行业的广泛应用,对计算机类人才的能力提出了新的要求。该文从新工科建设的角度出发,对计算机学科建设和人才培养问题进行了全方位探析,提出应首先通过思想教育,使学生和教师树立正确的意识,建立多维化的目标体系,再建立完整的制度保障体系和监控体系,确保培养目标的实现,同时应利用信息化教学手段提升教学质量和效率。