Language emergence can take multiple paths: Using motion capture to track axis use in Nicaraguan Sign Language

Research on emergent sign languages suggests that younger sign languages may make greater use of the z-axis, moving outwards from the body, than more established sign languages when describing the relationships between participants and events (Padden, Meir, Aronoff, and Sandler, 2010). This has been suggested to reflect a transition from iconicity rooted in the body (Meir, Padden, Aronoff, and Sandler, 2007) towards a more abstract schematic iconicity. We present the results of an experimental investigation into the use of axis by signers of Nicaraguan Sign Language (NSL). We analysed 1074 verb tokens elicited from NSL signers who entered the signing community at different points in time between 1974 and 2003. We used depth and motion tracking technology to quantify the position of signers’ wrists over time, allowing us to build an automated and continuous measure of axis use. We also consider axis use from two perspectives: a camera-centric perspective and a signer-centric perspective. In contrast to earlier work, we do not observe a trend towards increasing use of the x-axis. Instead we find that signers appear to have an overall preference for the z-axis. However, this preference is only observed from the camera-centric perspective. When measured relative to the body, signers appear to be making approximately equal use of both axes, suggesting the preference for the z-axis is largely driven by signers moving their bodies (and not just their hands) along the z-axis. We argue from this finding that language emergence patterns are not necessarily universal and that use of the x-axis may not be a prerequisite for the establishment of a spatial grammar.

Spatial Boundaries, Abounding Spaces

Colonialism advanced its project of territorial expansion by changing the very meaning of borders and space. The colonial project scripted a unipolar spatial discourse that saw the colonies as an extension of European borders. In his monograph, Mohit Chandna engages with narrations of spatial conflicts in French and Francophone literature and film from the nineteenth to the early twenty-first century. In literary works by Jules Verne, Ananda Devi, and Patrick Chamoiseau, and film by Michael Haneke, Chandna analyzes the depiction of ever-changing borders and spatial grammar within the colonial project. In so doing, he also examines the ongoing resistance to the spatial legacies of colonial practices that act as omnipresent enforcers of colonial borders. Literature and film become sites that register colonial spatial paradigms and advance competing narratives that fracture the dominance of these borders. Through its analyses Spatial Boundaries, Abounding Spaces shows that colonialism is not a finished project relegated to our past. Colonialism is present in the here and now, and exercises its power through the borders that define us.

Spatial Boundaries, Abounding Spaces

Colonialism advanced its project of territorial expansion by changing the very meaning of borders and space. The colonial project scripted a unipolar spatial discourse that saw the colonies as an extension of European borders. In his monograph, Mohit Chandna engages with narrations of spatial conflicts in French and Francophone literature and film from the nineteenth to the early twenty-first century. In literary works by Jules Verne, Ananda Devi, and Patrick Chamoiseau, and film by Michael Haneke, Chandna analyzes the depiction of ever-changing borders and spatial grammar within the colonial project. In so doing, he also examines the ongoing resistance to the spatial legacies of colonial practices that act as omnipresent enforcers of colonial borders. Literature and film become sites that register colonial spatial paradigms and advance competing narratives that fracture the dominance of these borders. Through its analyses Spatial Boundaries, Abounding Spaces shows that colonialism is not a finished project relegated to our past. Colonialism is present in the here and now, and exercises its power through the borders that define us.

Space as space and space as grammar

Research on narratives in an Australian language demonstrated surprising facts about speakers’ spatial orientation and knowledge both in the insistent use of morphologically hypertrophied spoken directional terminology and in accompanying gestures. Pursuing comparable phenomena in a Mayan language from the other side of the globe revealed correspondingly complex gestural devices for communicating about location and direction but with very different kinds of support from speech. Evidence from a new sign language, emerging in the same Mayan context, suggests that mechanisms for signing about space both resemble and depart from the gestural practices of the surrounding speech community. In particular, they invoke spatial “frames of reference” not used by speakers to sign about location and direction, and they employ signed “spatial grammar” to express syntactic argument structure.

Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

The authors present preliminary results on successfully training a recurrent neural network to learn a spatial grammar embodied in a data set, and then generate new designs that comply with the grammar but are not from the data set, demonstrating generalized learning. For the test case, the data were created by first exercising generative context-free spatial grammar representing physical layouts that included infeasible designs due to geometric interferences and then removing the designs that violated geometric constraints, resulting in a data set from a design grammar that is of a higher complexity context-sensitive grammar. A character recurrent neural network (char-RNN) was trained on the positive remaining results. Analysis shows that the char-RNN was able to effectively learn the spatial grammar with high reliability, and for the given problem with tuned hyperparameters, having up to 98% success rate compared to a 62% success rate when randomly sampling the generative grammar. For a more complex problem where random sampling results in only 18% success, a trained char-RNN generated feasible solutions with an 89% success rate. Further, the char-RNN also generated designs differing from the training set at a rate of over 99%, showing generalized learning.

Spatial Grammar-Based Recurrent Neural Network for Design Form and Behavior Optimization

A novel method has been developed to optimize both the form and behavior of complex systems. The method uses spatial grammars embodied in character-recurrent neural networks (char-RNNs) to define the system including actuator numbers and degrees of freedom, reinforcement learning to optimize actuator behavior, and physics-based simulation systems to determine performance and provide (re)training data for the char-RNN. Compared to parametric design optimization with fixed numbers of inputs, using grammars and char-RNNs allows for a more complex, combinatorial infinite design space. In the proposed method, the char-RNN is first trained to learn a spatial grammar that defines the assembly layout, component geometries, material properties, and arbitrary numbers and degrees of freedom of actuators. Next, generated designs are evaluated using a physics-based environment, with an inner optimization loop using reinforcement learning to determine the best control policy for the actuators. The resulting design is thus optimized for both form and behavior, generated by a char-RNN embodying a high-performing grammar. Two evaluative case studies are presented using the design of the modular sailing craft. The first case study optimizes the design without actuated surfaces, allowing the char-RNN to understand the semantics of high-performing designs. The second case study extends the first by incorporating controllable actuators requiring an inner loop behavioral optimization. The implications of the results are discussed along with the ongoing and future work.

A Spatial Grammar Method for the Computational Design Synthesis of Virtual Soft Locomotion Robots

Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. They lack rigid components that provide them with higher flexibility, and they have no joints that need protection from liquids or dirt. However, the hand-design of soft robots is often a lengthy trail-and-error process. This work presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The computational approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization. Here, designs are generated using a spatial grammar to explicitly guide the type of solutions generated and exclude infeasible designs. The soft material simulation method developed and integrated is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the method capabilities. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.

“Alive and kicking” – Moving through and diving into the Soviet kolkhoz and the East German LPG

The MODSCAPES project seeks an understanding of how modernist rural landscapes developed over time, in part calling for an understanding of the landscape as a set of intertwining layers assessed by local mapping. In addition, by understanding the spatial grammar of the landscape as well as perceiving it through different media, other aspects can be revealed which are not visible via mapping alone. The East-German and Baltic cases differ from other European examples by the fact that their existence ended nearly 30 years ago and residents and decision-makers from the time are still alive. Thus, we focused on the importance of actions carried out by residents in their everyday lives and ways to connect them with the respective space. If a landscape is understood by layers, then these actions form the “kebab skewers” metaphorically connecting them. Topographic maps from different periods formed the basis for the experiential data collected and interpreted in related steps accompanied by verbal commentaries. Firstly, we “dived” into the area using filming and field recordings simultaneously like a canvas to paint on and in the sense of a journey. Next, 360° surround films were shot at spots to simulate the view of a person turning around, followed by filming of situations representing everyday movement cycles in the area, such as going to work or taking children to school, which evoked an atmosphere of everyday life linking the space and people’s actions. Finally, go-along interviews were used to trigger and stimulate reflections and memories of residents to understand how the space impacted their experiences and perceptions. This process revealed facets of the daily life of the inhabitants, settlers or workers and their social interaction with the landscape, uncovering so far untouched places and unknown spatial relationships.