Polar Representation of 2D Image Using Complex Exponential Spiking Neuron Network
Why this work is in the frame
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Bibliographic record
Abstract
The paper introduces an innovative hybrid encoding method for images. It proposes a conversion process where the image is transformed from the conventional Cartesian coordinates representation (x and y) to a polar coordinates representation using complex numbers with magnitude ρ and angle θ. By doing so, the spatial information of pixel locations and the connections among adjacent pixels are preserved during the serialization process, and the frequency components of the image are effectively calculated and combined during Discrete Fourier Transform. Furthermore, the paper outlines the utilization of a spiking neural network (SNN) for representing and reconstructing the image. The SNN is constructed by combining the outputs of 128 complex exponential spiking neurons. Each spiking neuron corresponds to a specific frequency component of the image. By employing this hybrid encoding method and utilizing the SNN, the research aims to enhance image representation and reconstruction processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it