Computing ecosystems: neural networks and embedded hardware platforms

Presented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsEmbedded hardware platforms such as single-board computers (e.g., Raspberry Pi, Bela) or microcontrollers (e.g., Teensy, Arduino Uno) offer an entry point for beginners into physical computing. However, deploying neural networks into these platforms is challenging for various reasons: It requires lower-level software development skills, as machine learning toolkits are typically not incorporated into these platforms. Besides, the long compilation times burden debugging and quick prototyping and experimentation. Due to the low-resource nature of embedded hardware platforms, neural networks are usually trained on a host machine, which involves a back-and-forth of data, platforms and programming languages. We inquire how these computing ecosystems might be designed to facilitate prototyping and experimentation and integrate into existing programming workflows

Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform

Deploying deep learning models on embedded devices is an arduous task: oftentimes, there exist no platform-specific instructions, and compilation times can be considerably large due to the limited computational resources available on-device. Moreover, many music-making applications de- mand real-time inference. Embedded hardware platforms for audio, such as Bela, offer an entry point for beginners into physical audio computing; however, the need for cross- compilation environments and low-level software develop- ment tools for deploying embedded deep learning models imposes high entry barriers on non-expert users. We present a pipeline for deploying neural networks in the Bela embedded hardware platform. In our pipeline, we include a tool to record a multichannel dataset of sen- sor signals. Additionally, we provide a dockerised cross- compilation environment for faster compilation. With this pipeline, we aim to provide a template for programmers and makers to prototype and experiment with neural networks for real-time embedded musical applications

DMRN+16: Digital Music Research Network One-day Workshop 2021

DMRN+16: Digital Music Research Network One-day Workshop 2021 Queen Mary University of London Tuesday 21st December 2021 Keynote speakers Keynote 1. Prof. Sophie Scott -Director, Institute of Cognitive Neuroscience, UCL. Title: "Sound on the brain - insights from functional neuroimaging and neuroanatomy" Abstract In this talk I will use functional imaging and models of primate neuroanatomy to explore how sound is processed in the human brain. I will demonstrate that sound is represented cortically in different parallel streams. I will expand this to show how this can impact on the concept of auditory perception, which arguably incorporates multiple kinds of distinct perceptual processes. I will address the roles that subcortical processes play in this, and also the contributions from hemispheric asymmetries. Keynote 2: Prof. Gus Xia - Assistant Professor at NYU Shanghai Title: "Learning interpretable music representations: from human stupidity to artificial intelligence" Abstract Gus has been leading the Music X Lab in developing intelligent systems that help people better compose and learn music. In this talk, he will show us the importance of music representation for both humans and machines, and how to learn better music representations via the design of inductive bias. Once we got interpretable music representations, the potential applications are limitless

Agential Instruments Design Workshop

Physical and gestural musical instruments that take advantage of artificial intelligence and machine learning to explore instrumental agency are becoming more accessible due to the development of new tools and workflows specialised for mobility, portability, efficiency and low latency. This full-day, hands-on workshop will provide all of these tools to participants along with support from their creators, enabling rapid creative exploration of their applications a musical instrument design

Synthesis and biological activity of ferrocenyl indeno[1,2-c]isoquinolines as topoisomerase II inhibitors

International audienceThree series of indeno[1,2-c]isoquinolines bearing a ferrocenyl entity were synthesized and evaluated for DNA interaction, topoisomerase I and II inhibition, and cytotoxicity against breast human cancer cell lines. In the first and second series, the ferrocenyl scaffold was inserted as a linker between the two nitrogen atoms. In the last series, it was introduced at the end of the carbon chain. The present study showed that the ferrocenyl entity enhanced the topoisomerase II inhibition. Most compounds showed a potent growth inhibitory effect on MDA-MB-231 cell line with the IC50 in μM range

Synthesis, Structure, and Antiproliferative Activity of Ruthenium(II) Arene Complexes of Indenoisoquinoline Derivatives

International audienceNovel ruthenium complexes of indenoisoquinoline derivatives were synthesized and characterized. The structure of the complex 9 was determined by single-crystal X-ray crystallography. Ruthenium complexes displayed strong DNA interactions. The cytotoxic activity of the complexes was tested against five cancer cell lines (MDA-MB-231, MCF-7, HEK-293, HT-29, and DU-145)