We describe an exciting new application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB consists of a two-dimensional electrode array, and it manipulates droplets of liquid to automatically execute biochemical protocols for clinical chemistry. However, a major problem with DMFBs is that electrodes can degrade over time. The transportation of droplet transportation over these degraded electrodes can fail, thereby adversely impacting the integrity of the bioassay outcome. We demonstrated that the formulation of droplet transportation as an RL problem enables the training of deep neural network policies that can adapt to the underlying health conditions of electrodes and ensure reliable fluidic operations. We describe an RL-based droplet routing solution that can be used for various sizes of DMFBs. We highlight the reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. We show that the use of the RL approach on a simple micro-computer (Raspberry Pi 4) leads to acceptable performance for time-critical bioassays. We present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet routing problems on DMFBs. We present results on our study of electrode degradation using fabricated DMFBs. The study supports the degradation model used in the simulator.

Abstract While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based approach, with a single supervised dataset. Using these three datasets for training, we perform extensive experiments on both multitasking and prompting strategies. Compared to other systems trained on unsupervised parallel data, models trained on our weak classifier labeled dataset achieve state-of-the-art performance on the ASSET simplification benchmark. Our models also outperform previous work on sentence-level targeting. Finally, we establish how a handful of Large Language Models perform on these tasks under a zero-shot setting.

The micro-electrode-dot-array (MEDA) architecture provides precise droplet control and real-time sensing in digital microfluidic biochips. Previous work has shown that trapped charge under microelectrodes (MCs) leads to droplets being stuck and failures in fluidic operations. A recent approach utilizes real-time sensing of MC health status, and attempts to avoid degraded electrodes during droplet routing. However, the problem with this solution is that the computational complexity is unacceptable for MEDA biochips of realistic size. Consequently, in this work, we introduce a deep reinforcement learning (DRL)-based approach to bypass degraded electrodes and enhance the reliability of routing. The DRL model utilizes the information of health sensing in real time to proactively reduce the likelihood of charge trapping and avoid using degraded MCs. Simulation results show that our approach provides effective routing strategies for COVID-19 testing protocols. We also validate our DRL-based approach using fabricated prototype biochips. Experimental results show that the developed DRL model completed the routing tasks using a fewer number of clock cycles and shorter total execution time, compared with a baseline routing method. Moreover, our DRL-based approach provides reliable routing strategies even in the presence of degraded electrodes. Our experimental results show that the proposed DRL-based routing is robust to occurrences of electrode faults, as well as increases the lifetime and usability of microfluidic biochips compared to existing strategies.

The Adiabatic Quantum-Flux-Parametron (AQFP), which benefits from low power consumption and rapid switching, is one of the rising superconducting logics. Due to the rapid switching, the delay of the inputs of an AQFP gate is strictly specified so that additional buffers are needed to synchronize the delay. Meanwhile, to maintain the symmetry layout of gates and reduce the undesired parasitic magnetic coupling, the AQFP cell library adopts the minimalist design method in which splitters are employed for the gates with multiple fan-outs. Thus, an AQFP circuit may demand numerous splitters and buffers, resulting in a considerable amount of power consumption and delay. This provides a motivation for proposing an effective splitter and buffer insertion algorithm for the AQFP circuits. In this paper, we propose a dynamic programming-based algorithm that provides an optimal splitter and buffer insertion for each wire of the input circuit. Experimental results show that our method is fast, and has a 10% reduction of additional Josephson Junctions (JJs) in the complicated circuits compared with the state-of-the-art method.

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with very low energy dissipation. Each AQFP cell is driven by AC-power to serve as both power supply and clock signal. The clock signals trigger the data flow from one clock phase to the next clock phase, and the delay for each output in the same phase has to be equal. At the same time, the signal current attenuates as the wire becomes longer. When a wire exceeds a maximum length, the weak current causes incorrect data. Thus, rows of buffers have to be inserted as repeaters to satisfy both delay synchronization and wirelength constraint. These inserted buffers significantly increase the power consumption and also the total delay of AQFP circuits. In this paper, we propose an analytical strategy for AQFP placement (ASAP) to provide effective placement results that greatly reduce the number of additional inserted buffers. ASAP includes two main characteristics: 1) a new wire-length function for analytical global placement and 2) detailed placement including fixed-order Lagrangian relaxation and cell balancing algorithm. Experimental results show the efficiency of ASAP framework and a 53% reduction of buffers over the state-of-the-art method.