102,627 research outputs found
Generative AI for Controllable Protein Sequence Design: A Survey
The design of novel protein sequences with targeted functionalities underpins
a central theme in protein engineering, impacting diverse fields such as drug
discovery and enzymatic engineering. However, navigating this vast
combinatorial search space remains a severe challenge due to time and financial
constraints. This scenario is rapidly evolving as the transformative
advancements in AI, particularly in the realm of generative models and
optimization algorithms, have been propelling the protein design field towards
an unprecedented revolution. In this survey, we systematically review recent
advances in generative AI for controllable protein sequence design. To set the
stage, we first outline the foundational tasks in protein sequence design in
terms of the constraints involved and present key generative models and
optimization algorithms. We then offer in-depth reviews of each design task and
discuss the pertinent applications. Finally, we identify the unresolved
challenges and highlight research opportunities that merit deeper exploration.Comment: 9 page
Generative AI for Product Design: Getting the Right Design and the Design Right
Generative AI (GenAI) models excel in their ability to recognize patterns in
existing data and generate new and unexpected content. Recent advances have
motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to
professional practice across industries, including product design. While these
generative capabilities may seem enticing on the surface, certain barriers
limit their practical application for real-world use in industry settings. In
this position paper, we articulate and situate these barriers within two phases
of the product design process, namely "getting the right design" and "getting
the design right," and propose a research agenda to stimulate discussions
around opportunities for realizing the full potential of GenAI tools in product
design
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Radar is of vital importance in many fields, such as autonomous driving,
safety and surveillance applications. However, it suffers from stringent
constraints on its design parametrization leading to multiple trade-offs. For
example, the bandwidth in FMCW radars is inversely proportional with both the
maximum unambiguous range and range resolution. In this work, we introduce a
new method for circumventing radar design trade-offs. We propose the use of
recent advances in computer vision, more specifically generative adversarial
networks (GANs), to enhance low-resolution radar acquisitions into higher
resolution counterparts while maintaining the advantages of the low-resolution
parametrization. The capability of the proposed method was evaluated on the
velocity resolution and range-azimuth trade-offs in micro-Doppler signatures
and FMCW uniform linear array (ULA) radars, respectively.Comment: Accepted in EUSIPCO 2019, 5 page
High fidelity image counterfactuals with probabilistic causal models
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals
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