Cross-Dual Path Attention for Concurrent CSI-Based Applications in Indoor Environments
Bibliographic record
Abstract
Due to the omnipresence of radio frequency signals, the Channel State Information (CSI) can offer an alternate source to image, video, and other high-dimensional streams in a great many Internet-of-Things (IoT) applications. As a result, an ever increasing number of researchers are advocating for the use of passive CSI data for ranging, tracking, perception and automation across many domains such as robotics, healthcare, and surveillance. Specifically in indoor environments where movements cause classifiable effects, the CSI can be leveraged to provide a high-dimensional signal source for a broad set of applications including activity, gesture, pose, location, and orientation recognition. This task however remains a challenge on two accounts. On the one hand, the radio frequency channel is highly susceptible to environment changes and artifacts. On the other hand, there is a lack of robust models that cover the full range of applications for practical deployment. In this work, we focus on tackling these issues by proposing a novel cross-dual-path-attention architecture that is robust against environment variations and achieves high accuracy across multiple tasks in practical settings. Our experiments on multiple datasets verify that the proposed architecture consistently outperforms the state-of-the-art methods when tested for concurrent application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".