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People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several \"visual Turing tests\" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements—an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements—an observation that is consistent with “tipping” theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods—an observation that is consistent with the “invasion” theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.

Many computer models contain unknown parameters which need to be estimated using physical observations. Tuo and Wu (2014) show that the calibration method based on Gaussian process models proposed by Kennedy and O'Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol. 63 (2001) 425-464] may lead to an unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L₂ calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.

High-throughput computational approaches combining thermodynamic and electronic-structure methods with data mining and database construction are increasingly used to analyse huge amounts of data for the discovery and design of new materials. This Review provides an overall perspective of the field for a broad range of materials, and discusses upcoming challenges and opportunities. High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.

Nature provides many examples of self- and co-assembling protein-based molecular machines, including icosahedral protein cages that serve as scaffolds, enzymes, and compartments for essential biochemical reactions and icosahedral virus capsids, which encapsidate and protect viral genomes and mediate entry into host cells. Inspired by these natural materials, we report the computational design and experimental characterization of co-assembling, two-component, 120-subunit icosahedral protein nanostructures with molecular weights (1.8 to 2.8 megadaltons) and dimensions (24 to 40 nanometers in diameter) comparable to those of small viral capsids. Electron microscopy, small-angle x-ray scattering, and x-ray crystallography show that 10 designs spanning three distinct icosahedral architectures form materials closely matching the design models. In vitro assembly of icosahedral complexes from independently purified components occurs rapidly, at rates comparable to those of viral capsids, and enables controlled packaging of molecular cargo through charge complementarity. The ability to design megadalton-scale materials with atomic-level accuracy and controllable assembly opens the door to a new generation of genetically programmable protein-based molecular machines.

Inbreeding (mating between relatives) can dramatically reduce the fitness of offspring by causing parts of the genome to be identical by descent. Thus, measuring individual inbreeding is crucial for ecology, evolution and conservation biology. We used computer simulations to test whether the realized proportion of the genome that is identical by descent (IBDG) is predicted better by the pedigree inbreeding coefficient (FP) or by genomic (marker-based) measures of inbreeding. Genomic estimators of IBDG included the increase in individual homozygosity relative to mean Hardy-Weinberg expected homozygosity (FH), and two measures (FROH and FE) that use mapped genetic markers to estimate IBDG. IBDG was more strongly correlated with FH, FE and FROH than with FP across a broad range of simulated scenarios when thousands of SNPs were used. For example, IBDG was more strongly correlated with FROH, FH and FE (estimated with ⩾10 000 SNPs) than with FP (estimated with 20 generations of complete pedigree) in populations with a recent reduction in the effective populations size (from Ne=500 to Ne=75). FROH, FH and FE generally explained >90% of the variance in IBDG (among individuals) when 35 K or more SNPs were used. FP explained <80% of the variation in IBDG on average in all simulated scenarios, even when pedigrees included 20 generations. Our results demonstrate that IBDG can be more precisely estimated with large numbers of genetic markers than with pedigrees. We encourage researchers to adopt genomic marker-based measures of IBDG as thousands of loci can now be genotyped in any species.

In goal-oriented requirements engineering, goal models have been advocated to express stakeholder objectives and to capture and choose among system requirement candidates. A number of highly automated procedures have been proposed to analyze goal achievement and select alternative requirements using goal models. However, during the early stages of requirements exploration, these procedures are difficult to apply, as stakeholder goals are typically high-level, abstract, and hard-to-measure. Automated procedures often require formal representations and/or information not easily acquired in early stages (e.g., costs, temporal constraints). Consequently, early requirements engineering (RE) presents specific challenges for goal model analysis, including the need to encourage and support stakeholder involvement (through interactivity) and model improvement (through iterations). This work provides a consolidated and updated description of a framework for iterative, interactive, agent-goal model analysis for early RE. We use experiences in case studies and literature surveys to guide the design of agent-goal model analysis specific to early RE. We introduce analysis procedures for the i* goal-oriented framework, allowing users to ask “what if?” and “are certain goals achievable? how? or why not?” The i* language and our analysis procedures are formally defined. We describe framework implementation, including model visualization techniques and scalability tests. Industrial, group, and individual case studies are applied to test framework effectiveness. Contributions, including limitations and future work, are described.

Natural recombination combines pieces of preexisting proteins to create new tertiary structures and functions. We describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C. High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models. This method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds.

Motivated by a computer experiment for the design of a rocket booster, this article explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details that make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, we show that our approach is effective in other arenas as well.

Heat stress (HS) is a leading cause of weather-related human mortality. As temperatures continue to increase due to climate change, HS is expected to worsen. HS can be magnified in urban areas because of the urban heat island effect. We use an urban canyon model coupled to a land surface model to quantify present-day and projected mid-21st century rural and urban HS for boreal summer over the U.S. and southern Canada and examine the effects of three urban density classes on HS. Five indices of HS are implemented in the model [the NWS Heat Index (HI), Apparent Temperature (AT), Simplified Wet Bulb Globe Temperature, Humidex, and Discomfort Index]. The present-day urban-rural contrast in HS differs according to which index is used. The HI and Humidex have higher urban-rural HS for all density classes than defined by temperature alone. Future urban HS is amplified by 0.5–1.0 °C for the AT, HI, and Humidex compared to temperature alone. For four cities examined in further detail, climate change by mid-century increases the number of high HS days and nights in both rural and urban areas, the magnitude being highly dependent on HS index, urban density class, and each city’s climatic setting. Houston exhibits noteworthy mid-century increases in high heat stress nights, with more than half of summer nights qualifying as high HS in not only urban areas but also rural areas, indicating the need to consider vulnerability and adaptive capacity of both rural and urban populations in the context of climate change.