Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multitasking is necessary and domain shifts are exhibited. Recently, instructional prompts have shown significant improvement towards multitask generalization; however, the effect of instructional prompts and Multi-Task Learning (MTL) has not been systematically studied in the biomedical domain. Motivated by this, this paper explores the impact of instructional prompts for biomedical MTL. We introduce the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X) various categories. Using this meta-dataset, we propose a unified model termed as In-BoXBART, that can jointly learn all tasks of the BoX without any task-specific modules. To the best of our knowledge, this is the first attempt to propose a unified model in the biomedical domain and use instructions to achieve generalization across several biomedical tasks. Experimental results indicate that the proposed model: 1) outperforms single-task baseline by ∼3% and multitask (without instruction) baseline by ∼18% on an average, and 2) shows ∼23% improvement compared to single-task baseline in few-shot learning (i.e., 32 instances per task) on an average. Our analysis indicates that there is significant room for improvement across tasks in the BoX, implying the scope for future research direction. 1
This paper presents a passive 13.56 MHz RFID transponder frontend design using 0.18 µm CMOS Technology for implantable biosensor applications. Power is provided to the system through a dual output full wave rectifier that provides power at two different voltage levels; the low level to the transponder frontend to reduce its power consumption and the high level to the biosensor to increase its dynamic range. The low voltage operation of the frontend is supplemented further by a current starved design reducing its power consumption to a minimal and leaving most available power to the biosensor. The design is verified using HSPICE Simulation showing a maximum frontend power consumption of only 6.5 µW and leaving at least 88% of the available power for the biosensor's operation.
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