Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
Illinois Institute of Technology, Chicago, Illinois | Universidad Politécnica de Madrid, Madrid, Spain
30秒速读
IN SHORT: This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus crickets, which is essential for selective breeding, optimized reproduction ratios, and nutritional differentiation.
核心创新
- Methodology First integrated system combining embedded computer vision (YOLOv8 nano) with physical actuation for real-time cricket sex sorting, achieving 86.8% overall sorting accuracy.
- Methodology Development of a custom balanced dataset (597 training images) with controlled illumination variability and data augmentation, achieving mAP@0.5 of 0.977 with minimal misclassification (2.3% for females, 2.7% for males).
- Biology Quantitative analysis revealing sex-dependent behavioral differences: males move faster (median 39.5 mm/s vs 18.8 mm/s for females) and are more sensitive to actuator noise, affecting sorting efficiency under different stress conditions.
主要结论
- The system achieved 86.8% overall sorting accuracy across four experiments, with performance varying from 83% under high-stress conditions to 94% under low-stress conditions.
- Movement speed significantly impacts sorting accuracy: misclassified crickets had median speeds of 75.0 mm/s compared to 20.3 mm/s for correctly classified individuals (p<0.05 based on distribution analysis).
- The YOLOv8 nano model demonstrated excellent detection performance with mAP@0.5 of 0.977 and class-wise precision/recall of 0.857-0.878, proving suitable for resource-constrained edge deployment.
摘要: The global demand for sustainable protein sources is driving increasing interest in edible insects, with Acheta domesticus (house cricket) identified as one of the most suitable species for industrial production. Current farming practices typically rear crickets in mixed-sex populations without automated sex sorting, despite potential benefits such as selective breeding, optimized reproduction ratios, and nutritional differentiation. This work presents a low-cost, real-time system for automated sex-based sorting of Acheta domesticus, combining computer vision and physical actuation. The device integrates a Raspberry Pi 5 with the official Raspberry AI Camera and a custom YOLOv8 nano object detection model, together with a servo-actuated sorting arm. The model reached a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.977 during testing, and real-world experiments with groups of crickets achieved an overall sorting accuracy of 86.8%. These results demonstrate the feasibility of deploying lightweight deep learning models on resource-constrained devices for insect farming applications, offering a practical solution to improve efficiency and sustainability in cricket production.