Bibliographic record
Abstract
In this thesis, I have implemented a GPU (graphics processor unit) based sequencing algorithm that finds a sequence or order among data points to optimize a given objective. I have studied the sequencing algorithm as a path planner for an unmanned aerial vehicle (UAV) and also, a basecaller for a miniature DNA sequencer. Parallel implementation utilizing GPU enables faster processing and decision making that are important when data is quite large and a real-time response is critical e.g., in UAV based transportation. The goal of using UAV in my thesis is to construct a wireless sensor network in a remote location by deploying wireless sensor nodes. The proposed path planner, also known as a sequencer, is designed to find the shortest path that is also a safe path using travelling salesman problem. A path is considered safe when the vehicle would not collide with any obstacle. Two sets of heuristic algorithms, one for generating a sequence of waypoints (sequence generator) and another one for constructing a path between two waypoints (path explorer), are used to find the near optimal solution. The highly-parallel multicore GPU is used for the real-time implementation that offloads compute-intensive portions from the traditional CPU to the GPU to make the decision-making process faster. In this thesis, the parallel execution of the sequence generator and the path explorer achieved a 4.82x and 164x speed-up compared to the CPU-only approach respectively. The second sequencer is a palm-sized miniature DNA sequencer, the so-called MinION device. In the case of the MinION, a vast multitude of DNA strands are introduced into the device and converted into noisy electronic time-series signals; these measurements are essentially physical signatures related to the molecular make-up of the sensed DNA. Among a long "pipeline" of analysis steps to be performed on such measurement sequences, the first, and arguably most intensive, is the so-called basecalling step which analyzes the time-series and converts it into the equivalent monomeric base of the DNA under test using the Viterbi algorithm.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".