Paper List
-
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...
-
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 ...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
Institute of Computational Science and Technology, Guangzhou University, China | School of Computer Science and Technology, Wuhan University of Science and Technology, China | School of Computer Science and Technology, Dalian University of Technology, China | School of Computing Science, Peking University, China
30秒速读
IN SHORT: This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalable ternary architecture.
核心创新
- Methodology Proposes a novel Competitive Blocking (CB) circuit that leverages differential reaction kinetics (k2 ≫ k1, k3) to dynamically select and block reaction pathways for precise carry information management.
- Methodology Introduces a ternary (base-3) adder architecture, moving beyond binary systems, which inherently reduces the frequency of carry propagation and increases single-bit information density.
- Methodology Implements a Dynamic Concentration Adjustment (CA) strategy, applying chemical equilibrium principles to optimize reactant ratios and signal transmission, enabling significant bit-width extension.
主要结论
- The CB circuit reliably performs ternary full-adder logic, with experimental validation showing successful 10-bit addition operations.
- The integrated CA strategy enables the adder to scale to 17-bit addition, representing a massive increase in computational scale.
- The architecture achieves a 'scale/strand' metric improvement of 2,405,552x compared to a recent state-of-the-art binary DNA adder capable of only 4 consecutive carries.
摘要: DNA adder circuits are programmable reaction networks that process DNA molecular inputs to compute a sum and serve as essential components for digital computation. Currently, DNA adders primarily focus on binary addition. While efforts extend the operational bit-width by minimizing the number of DNA strands and developing carry-transmission mechanisms, challenges such as the susceptibility of carrying information to attenuation and the limited expressive capacity of the binary system impose significant constraints on computational scale. This paper proposes a scalable ternary adder architecture by introducing an innovative competitive blocking (CB) circuit. The architecture employs a dual cooperative optimization strategy that significantly enhances single-bit computational capacity and incorporates a dynamic concentration adjustment (CA) to effectively broaden the computational bit-width. Consequently, a significant increase in molecular computing scale is achieved compared to previous binary adders. Biochemical experimental results indicate that the CB circuit effectively outputs the ternary full-adder bit and successfully performs 10-bit addition. Furthermore, by implementing the CA strategy, this adder can be further extended to support 17-bit addition. This research provides a novel methodological foundation for advancing DNA computing technologies and offers promising potential for scalable digital computing applications.