Scripting Sounds and Sounding Scripts

Building from the rhymed prose register, chapter four analyzes the ways laments about Arabic writing have shaped practices of phatic connection in Fez. I look at the ways Fassis engaged darīja writing as a blending of multisensory channels tied to specific media platforms: folklore books, WhatsApp, advertising billboards, and newsprint. Instead of foregrounding the aural/spoken soundscape or the visual/graphic linguascape, I examine the intertwining of these sensorial channels in the sounding of darīja script and scripting of darīja sounds by reading subjects, everyday Moroccans who authorized themselves to weigh in on the politics of writing. Scholars have written about Arabic soundscapes, the acoustic environments, listening practices, and ritual sounding in which Arabic shapes public discourse and Muslim subjects. Others have focused on the emergence of Arabic dialect writing movements as expressions of political movements, local advertising campaigns, and youth-driven social change movements. Both the soundscape and darīja writing literatures hint at the multisensory channel practices and ideologies mobilized to make Moroccan persons, and they include laments about modality failures that motivated writing changes in the last decade. In the face of debates about the role of language in Moroccan national identity, Fassi everyday scriptic heterogeneity pointed to a practice of ambivalence toward written darīja in specific media platforms (billboards, websites, and mobile apps), but not others (books and newsprint). The platforms of writing mattered to the phatic work of making Moroccans in Fez.

Size Doesn’t Matter—What Can Handwriting Tell Us About the Brain?

When you write on the blackboard, you use much larger movements than when you write in a notebook. Yet, the large letters written on the blackboard will appear very similar to the small letters written in the notebook. How does your brain generate these different writing movements? Does it use a single set of commands for both the notebook and blackboard—or multiple commands? In this article, we will present an experiment in which we measured brain activations of people while they were writing letters of different sizes and discuss what we can learn from this experiment about the brain commands that generate these movements.

“She Is So Fidgety”

Chorea manifests as involuntary, often contnuous, unpredictable, and involuntary dance-like movements. Patients with chorea are often unaware that they have involuntary movements. Others may try to incorporate the movement into a semipurposeful action (parakinesia). Chorea is usually worse with mental activity or emotion. Physical activity may also exacerbate chorea. The presence of “motor impersistence” is typical of chorea. Sometimes patients can also make unintentional sounds referred to as hyperkinetic dysarthria. Chorea disappears during sleep. Ballism is considered a type of chorea with a more proximal distribution and larger movements. Athetosis is a term formally used for chorea with slow writing movements in the distal limbs, but it is not considered a specific entity of chorea anymore. The most important genetic cause of chorea in adulthood is Huntington’s disease, and genetic testing should be considered as a first step in all patients with adult-onset chorea if no secondary cause is found.

Robots Learn Writing

This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP) method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.