Re: Decoding Design
Raingarda Krzynowek
Designers and architects are faced with complex challenges when creating objects such as furniture, cars, and buildings in our 3D world. For example, chairs must be comfortable to sit on, while cars need to be aerodynamic. To ensure that shapes are functional in real-world settings, designers use computer-aided design software to generate 3D models and simulation software to test their designs, such as a wind simulation on a 3D model of a car to ensure it flows well.
While 3D modeling provides precise and realistic depictions of objects and surroundings, designers often delay using it until their ideas are fully developed. This is in part because creating 3D shapes using 2D input devices such as a computer mouse on a flat surface is difficult.
Adrien Bousseau and his team set out to tackle this problem by aiming to enhance the early stages of design exploration. Their goal was to develop a method for generating 3D models from sketches, allowing designers to convey their concepts to computers in the same manner as they would to clients or colleagues. This would enable them to assess, for instance, the aerodynamic properties of a car's bodywork immediately following its sketching.
Bousseau and his team looked at 400 drawings to examine the geometric properties of various types of lines, and used the techniques developed by professional designers in order to represent 3D shapes. A first stream of research we did was to try to identify many of those geometric constraints, from lines in the drawings, and then run an algorithm that searches for the shape and respects those constraints as best as possible.
Bousseau and his team have developed the first methods that enable the automatic reconstruction of freehand design drawings made on a pen tablet. Prior to their research, existing techniques for transforming drawings into 3D models were inadequate because they required significant effort from the designer, according to Bousseau.
Go Media has been a staple in the Cleveland graphic design, logo design, and website design for more than two decades. We understand marketing, brand building, and beautiful design - and we're passionate about helping each of our clients look - and function - at their best.
We believe in creating remarkable design experiences, going beyond the boundaries of technology to create provocative web design & branding that gets noticed. Our clients trust us to deliver inspired creativity unique to them, service that's personal and responsive, and expectations and costs that are transparent. We strive to create work that's not only authentic and bold - but impactful.
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.
Objective. Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding.Approach. We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance in long term (over several months).Main results. We demonstrate a multiplication-free fixed-point spike detection algorithm with an average detection accuracy of 97% across different noise levels on a synthetic dataset and the lowest hardware complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.Significance. Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.
This paper investigates the role of stimuli and respective levels of abstraction in design briefs and the implications for client-designer expectations alignment. This paper examines design briefs in professional settings in two Danish companies, from the perspectives of the client who creates a brief and the external designer who responds to a brief. The method consists in analysing the design briefs and categorising content, type of stimuli and level of abstraction, followed by interviews with the sender and receiver of the brief. According to the findings, the definition of a clear solution space in the design brief occurs when there is a coherent relationship between the level of abstraction and the presented type of stimuli, which optimises resources in concept development. When coherency is not achieved, that is, when different stimuli are included with the incorrect level of abstraction that allows for broad interpretations, it is counterproductive.
Busch, S.,Sander Jensen, N.,and Barros, M.(2023) Decoding design briefs: The role of abstraction levels in textual and visual stimuli, in Holmlid, S., Rodrigues, V., Westin, C., Krogh, P. G., Mäkelä, M., Svanaes, D., Wikberg-Nilsson, Å (eds.), Nordes 2023: This Space Intentionally Left Blank, 12-14 June, Linköping University, Norrköping, Sweden.
Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.
Here, we develop a similar simulation-based approach to predict online performance and validate it by comparing its predictions to held-out closed-loop iBCI data from three clinical trial participants. Our approach improves upon the OPS study by being able to run entirely on the computer (requiring no input from a human volunteer) and by enabling user-specific performance predictions. This expands the utility of the approach by enabling a rapid search across more parameters than would be possible with human volunteers. It also allows the simulation approach to be used in a clinical setting to customize the decoding parameters to suit a given iBCI user. Although several other studies have also successfully employed computer simulations of iBCI control to make qualitative insights (e.g.22,25,26), we are aware of no prior work that has demonstrated an ability to simulate iBCI control with the accuracy required for quantitative parameter selection and design.
aa06259810