Design patterns have proven useful in many creative fields, providing content creators with archetypal, reusable guidelines to leverage in projects. Creating such patterns, however, is a time-consuming, manual process, typically relegated to a few experts in any given domain. In this paper, we describe an algorithmic method for learning design patterns directly from data using techniques from natural language processing and structured concept learning. Given a set of labeled, hierarchical designs as input, we induce a probabilistic formal grammar over these exemplars. Once learned, this grammar encodes a set of generative rules for the class of designs, which can be sampled to synthesize novel artifacts. We demonstrate the method on geometric models and Web pages, and discuss how the learned patterns can drive new interaction mechanisms for content creators.

Enabling ordinary people to create high-quality 3D models is a long-standing problem in computer graphics. In this work, we draw from the literature on design and human cognition to better understand the design processes of novice and casual modelers, whose goals and motivations are often distinct from those of professional artists. The result is a method for creating exploratory modeling tools, which are appropriate for casual users who may lack rigidly-specified goals or operational knowledge of modeling techniques. Our method is based on parametric design spaces, which are often high dimensional and contain wide quality variations. Our system estimates the distribution of good models in a space by tracking the modeling activity of a distributed community of users. These estimates drive intuitive modeling tools, creating a self-reinforcing system that becomes easier to use as more people participate. We present empirical evidence that the tools developed with our method allow rapid creation of complex, high-quality 3D models by users with no specialized modeling skills or experience. We report analyses of usage patterns garnered throughout the year-long deployment of one such tool, and demonstrate the generality of the method by applying it to several design spaces.

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Figure 1: The table-chair sets, arm chairs, plants, shelves, and floor lamps in this coffee shop were arranged using our locally annealed reversible jump MCMC sampling method. The users don't need to specify the number of objects beforehand. Abstract We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples from transdimensional distributions encoding complex constraints. We use factor graphs, a type of graphi-cal model, to encode constraints as factors. Our proposed MCMC method, called locally annealed reversible jump MCMC, exploits knowledge of how dimension changes affect the structure of the factor graph. We employ a sequence of annealed distributions during the sampling process, allowing us to explore the state space across different dimensionalities more freely. This approach is motivated by the application of layout synthesis where relationships between objects are characterized as constraints. In particular, our method addresses the challenge of synthesizing open world layouts where the number of objects are not fixed and optimal configurations for different numbers of objects may be drastically different. We demonstrate the applicability of our approach on two open world layout synthesis problems: coffee shops and golf courses.

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This paper presents a label-free affinity-based capacitive biosensor using interdigitated electrodes. Using an optimized process of DNA probe preparation to minimize the effect of contaminants in commercial thiolated DNA probe, the electrode surface was functionalized with the 24-nucleotide DNA probes based on the West Nile virus sequence (Kunjin strain). The biosensor has the ability to detect complementary DNA fragments with a detection limit down to 20 DNA target molecules (1.5 aM range), making it suitable for a practical point-of-care (POC) platform for low target count clinical applications without the need for amplification. The reproducibility of the biosensor detection was improved with efficient covalent immobilization of purified single-stranded DNA probe oligomers on cleaned gold microelectrodes. In addition to the low detection limit, the biosensor showed a dynamic range of detection from 1 μL−1 to 105 μL−1 target molecules (20 to 2 million targets), making it suitable for sample analysis in a typical clinical application environment. The binding results presented in this paper were validated using fluorescent oligomers.

In this paper, a triazine‐based flame retardant (TAT) was synthesized from cyanuric chloride and aniline. Its chemical structure was characterized by Fourier transform infrared (FTIR) spectroscopy, 1H nuclear magnetic resonance, and elemental analysis. Two kinds of novel intumescent flame‐retardant epoxy systems were obtained with the incorporation of TAT and 9,10‐dihydro‐9‐oxa‐10‐phosphaphenanthrene‐10‐oxide (DOPO) or hexa‐phenoxy‐cyclotriphosphazene (HPCP). The flame retardancy of the obtained epoxy samples was evaluated using limited oxygen index, vertical burning (UL94), and cone calorimeter tests. The results indicated that there was a synergistic effect between TAT and DOPO or HPCP. The flame‐retardant mechanism was investigated by thermogravimetric analysis (TGA), thermogravimetric analysis/infrared spectrometry (TGA‐FTIR) coupled with the morphology and chemical analysis of the char residues. During combustion, DOPO or HPCP decomposed to release phosphorus‐containing free radicals with quenching effect. The morphology study showed that the introduction of DOPO or HPCP promoted the carbonization of epoxy matrix and the formation of a phosphorus‐containing viscous char layer, while the pyrolysis gases derived from the decomposition of TAT caused the char layer to expand. The main reason of the promotion of flame retardancy of epoxy samples was that the simultaneous addition of TAT and DOPO or HPCP led to the formation of a compact and intumescent char layer that restricted the transfer of heat and combustible volatiles and simultaneously protected the underlying matrix.

Traditional metal-oxide semiconductor devices are inadequate for use in artificial neural networks (ANNs) owing to their high power consumption, complex structures, and difficult fabrication techniques. Resistive random access memory (RRAM) is a promising candidate for ANNs owing to its simple structure, low power consumption, and excellent compatibility with CMOS. Moreover, it can mimic synaptic functions because of its multilevel resistive switching (RS) behavior. Herein, we demonstrate highly uniform RS and a high on/off ratio of RRAM based on graphene oxide by embedding gold nanoparticles into the device. This allowed reliable multilevel storage. Further, multilevel RRAM based on spike-timing-dependent-plasticity learning rules was used for image pattern recognition. These findings may offer a route to develop reliable digital memristors for ANNs.