Sinkless: A Preliminary Study of Stress Propagation in Group Project Social Networks using a Variant of the Abelian Sandpile Model

We perform social network analysis on 53 students split over three semesters and 13 groups, using conventional measures like eigenvector centrality, betweeness centrality, and degree centrality, as well as defining a variant of the Abelian Sandpile Model (ASM) with the intention of modeling stress propagation in the college classroom. We correlate the results of these analyses with group project grades received; due to a small or poorly collected dataset, we are unable to conclude that any of these network measures relates to those grades. However, we are successful in using this dataset to define a discrete, recursive, and more generalized variant of the ASM. Abelian Sandpile Model, College Grades, Self-organized Criticality, Sinkless Sandpile Model, Social Network Analysis, Stress PropagationComment: 11 pages, 8 figure

PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network

Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music audio from musical scores. Directly synthesizing audio with sound sample libraries often leads to mechanical and deadpan results, since musical scores do not contain performance-level information, such as subtle changes in timing and dynamics. Moreover, while the task may sound like a text-to-speech synthesis problem, there are fundamental differences since music audio has rich polyphonic sounds. To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the piano rolls and an audio representation of music called the spectrograms. The model consists of two subnets: the ContourNet, which uses a U-Net structure to learn the correspondence between piano rolls and spectrograms and to give an initial result; and the TextureNet, which further uses a multi-band residual network to refine the result by adding the spectral texture of overtones and timbre. We train the model to generate music clips of the violin, cello, and flute, with a dataset of moderate size. We also present the result of a user study that shows our model achieves higher mean opinion score (MOS) in naturalness and emotional expressivity than a WaveNet-based model and two commercial sound libraries. We open our source code at https://github.com/bwang514/PerformanceNetComment: 8 pages, 6 figures, AAAI 2019 camera-ready versio

Effects of Low Intensity Focused Ultrasound on Liposomes Containing Channel proteins.

The ability to reversibly and non-invasively modulate region-specific brain activity in vivo suggests Low Intensity Focused Ultrasound (LIFU) as potential therapeutics for neurological dysfunctions such as epilepsy and Parkinson's disease. While in vivo studies provide evidence of the bioeffects of LIFU on neuronal activity, they merely hint at potential mechanisms but do not fully explain how this technology achieves these effects. One potential hypothesis is that LIFU produces local membrane depolarization by mechanically perturbing the neuronal cell membrane, or activating channels or other proteins embedded in the membrane. Proteins that sense mechanical perturbations of the membrane, such as those gated by membrane tension, are prime candidates for activating in response to LIFU and thus leading to the neurological responses that have been measured. Here we use the bacterial mechanosensitive channel MscL, which has been purified and reconstituted in liposomes, to determine how LIFU may affect the activation of this membrane-tension gated channel. Two bacterial voltage-gated channels, KvAP and NaK2K F92A channels were also studied. Surprisingly, the results suggest that ultrasound modulation and membrane perturbation does not induce channel gating, but rather induces pore formation at the membrane protein-lipid interface. However, in vesicles with high MscL mechanosensitive channel concentrations, apparent decreases in pore formation are observed, suggesting that this membrane-tension-sensitive protein may serve to increase the elasticity of the membrane, presumably because of expansion of the channel in the plane of the membrane independent of channel gating

The Role of Sketch Finish and Style in User Responses to Early Stage Design Concepts

Conceptual sketches of design alternatives are often employed as a tool for eliciting feedback from design stakeholders, including potential end-users. However, such sketches can vary widely in their level of finish and style, thus potentially affecting how users respond to a concept. This paper presents a study of user responses to three objects drawn in styles ranging from rough hand sketches to CAD drawings. This study also considers the amount of design time required to create the sketches. Results show that respondents generally ranked realistic, “clean” hand sketches the highest over other types of sketches, particularly “rough” sketches. These types of sketches took longer than other types of hand sketches to create, but were still much faster than CAD renderings. Results also suggest that the complexity and familiarity of an object can influence how users respond to a sketch.National Science Foundation (U.S.) (Award CMMI-0830134

Enabling Conversational Interaction with Mobile UI using Large Language Models

Conversational agents show the promise to allow users to interact with mobile devices using language. However, to perform diverse UI tasks with natural language, developers typically need to create separate datasets and models for each specific task, which is expensive and effort-consuming. Recently, pre-trained large language models (LLMs) have been shown capable of generalizing to various downstream tasks when prompted with a handful of examples from the target task. This paper investigates the feasibility of enabling versatile conversational interactions with mobile UIs using a single LLM. We propose a design space to categorize conversations between the user and the agent when collaboratively accomplishing mobile tasks. We design prompting techniques to adapt an LLM to conversational tasks on mobile UIs. The experiments show that our approach enables various conversational interactions with decent performances, manifesting its feasibility. We discuss the use cases of our work and its implications for language-based mobile interaction