Interaction as enquiry: learning with layered dynamic media

The interactivity afforded to the learner by the computer based media is often fairly restricted. This may be by design choice but is often brought about by resource constraints. The new layered media forms give educational media developers an opportunity to provide richer forms of interactivity, and some of the issues related to changes in the way that interactive materials might be created are discussed. The distinction is made between the interface viewed simply as an information source and the interface regarded as an environment to be manipulated and explored

Compartmentalization of incompatible reagents within Pickering emulsion droplets for one-pot cascade reactions

It is a dream that future synthetic chemistry can mimic living systems to process multistep cascade reactions in a one-pot fashion. One of the key challenges is the mutual destruction of incompatible or opposing reagents, for example, acid and base, oxidants and reductants. A conceptually novel strategy is developed here to address this challenge. This strategy is based on a layered Pickering emulsion system, which is obtained through lamination of Pickering emulsions. In this working Pickering emulsion, the dispersed phase can separately compartmentalize the incompatible reagents to avoid their mutual destruction, while the continuous phase allows other reagent molecules to diffuse freely to access the compartmentalized reagents for chemical reactions. The compartmentalization effects and molecular transport ability of the Pickering emulsion were investigated. The deacetalization–reduction, deacetalization–Knoevenagel, deacetalization–Henry and diazotization–iodization cascade reactions demonstrate well the versatility and flexibility of our strategy in processing the one-pot cascade reactions involving mutually destructive reagents

Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first two authors contributed equally to this work. Project page: http://visualdynamics.csail.mit.ed

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.Comment: The first two authors contributed equally to this wor

Unsupervised Discovery of Parts, Structure, and Dynamics

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor

Design Gateway: Pedagogical Discussion of a Second-Year Industrial Design Studio

This presentation was part of the session : Pedagogy: Procedures, Scaffolds, Strategies, Tactics24th National Conference on the Beginning Design StudentMost industrial design programs focus the beginning design curriculum on the learning of core design principles. These core principles are seen as not specific to any one discipline (architecture, industrial design, interior design, etc.), but rather as fundamentals germane to all design fields. These core principles focus on the analysis of built artifact (structures, products, systems) to develop an understanding of geometry, structure and composition through looking and exploring. Students develop skills in representing, communicating and analyzing what they see and experience. These skills are nurtured in early studios. As students move into later studios, more discipline-specific knowledge and skills are integrated into their educational pedagogy. In the beginning years of design education, there is a transition from the learning of general 'core' design fundamentals to specialized principles that is inherent to their specific disciplines. As students move from abstract ideas to 'real-world' projects, they seem to have difficulty transitioning between the abstract concepts they previously learned and reality that requires application to new settings [1]. Students perceive learned concepts as specific to a particular studio project, rather than realize that design education is a continuum of practiced principles [1]. This presents a disconnect between knowledge transfer from one studio project to the next. The curriculum of the second-year industrial design studio at the Georgia Institute of Technology is designed to address this disconnect and help students successfully transition from the core design fundamentals to industrial design knowledge. Throughout the second year education, students engage in the making and communication of form and they do it through design exercises dealing with the fundamentals as well as knowledge base, both simultaneously and repeatedly, According to ----, a design education that offers a component of repetitive experience encourages students to be cognizant of the iterative nature of both the design process as well as design education [2]. This paper discusses the approach, designed by the authors, evident in the sophomore-year industrial design curriculum at Georgia Tech. While emphasis is placed on rigor, exploration and articulation of concepts throughout the studio period, this approach adopts a pedagogy based on a series of modules that scaffold the introduction of new concepts with the reinforcement of previously learned ones. Individual modules follow a path of concept introduction (lecture), analysis, practice, and finally refinement. Upon completion of several modules, students engage in a 'module project' which demonstrates synthesis and realization of the learned concepts. A final semester-end design project provides for aggregation and demonstration of all subject matter learned throughout the semester. This pedagogical approach bridges the gap of disconnect between previous studios and promotes a continuous layering and practice of beginning design fundamentals