Drug discovery is a complex process that involves sequentially screening and
examining a vast array of molecules to identify those with the target
properties. This process, also referred to as sequential experimentation, faces
challenges due to the vast search space, the rarity of target molecules, and
constraints imposed by limited data and experimental budgets. To address these
challenges, we introduce a human-in-the-loop framework for sequential
experiments in drug discovery. This collaborative approach combines human
expert knowledge with deep learning algorithms, enhancing the discovery of
target molecules within a specified experimental budget. The proposed algorithm
processes experimental data to recommend both promising molecules and those
that could improve its performance to human experts. Human experts retain the
final decision-making authority based on these recommendations and their domain
expertise, including the ability to override algorithmic recommendations. We
applied our method to drug discovery tasks using real-world data and found that
it consistently outperforms all baseline methods, including those which rely
solely on human or algorithmic input. This demonstrates the complementarity
between human experts and the algorithm. Our results provide key insights into
the levels of humans' domain knowledge, the importance of meta-knowledge, and
effective work delegation strategies. Our findings suggest that such a
framework can significantly accelerate the development of new vaccines and
drugs by leveraging the best of both human and artificial intelligence.

药物研发是一个复杂的过程，本文介绍了一种人工智能与人类专家知识相结合的人机协作框架，用于药物研发中的顺序实验，以提高针对目标分子的发现效率。研究结果表明，这种框架能够有效加速新疫苗和药物的开发，充分利用人类专家和人工智能的优势。

顺序实验中的协同智能：药物发现的人机交互框架

Collaborative Intelligence in Sequential Experiments: A  Human-in-the-Loop Framework for Drug Discovery

Large language models (LLMs) can label data faster and cheaper than humans
for various NLP tasks. Despite their prowess, LLMs may fall short in
understanding of complex, sociocultural, or domain-specific context,
potentially leading to incorrect annotations. Therefore, we advocate a
collaborative approach where humans and LLMs work together to produce reliable
and high-quality labels. We present MEGAnno+, a human-LLM collaborative
annotation system that offers effective LLM agent and annotation management,
convenient and robust LLM annotation, and exploratory verification of LLM
labels by humans.

大型语言模型（LLMs）可比人类更快速和廉价地对各种 NLP 任务进行数据标注。然而，LLMs 在理解复杂的社会文化或领域特定的上下文方面可能存在问题，导致错误的标注。因此，我们主张人类和 LLMs 合作的方法，以产生可靠和高质量的标签。我们介绍了 MEGAnno+，这是一个人 - LLM 协作标注系统，提供了有效的 LLM 代理和标注管理、便捷和稳健的 LLM 标注，以及人类对 LLM 标签的探索性验证。

MEGAnno+: 一个人类 - LLM 协作的标注系统

MEGAnno+: A Human-LLM Collaborative Annotation System

Due to edge device resource constraints and different characteristics of deep
neural network (DNN) models, it is a big challenge to optimize DNN inference
performance in terms of energy consumption and inference latency on edge
devices. In addition to the dynamic voltage frequency scaling (DVFS) technique,
the edge-cloud architecture provides a collaborative approach to efficient DNN
inference. However, current edge-cloud collaborative inference methods have not
optimized various compute resources on edge devices. Thus, we propose DVFO, a
novel DVFS-enabled edge-cloud collaborative inference framework, which jointly
optimize DVFS and offloading parameters via deep reinforcement learning (DRL).
Specifically, DVFO automatically co-optimizes 1) CPU, GPU and memory
frequencies of edge devices, and 2) feature maps to be offloaded to cloud
servers. In addition, it leverages a thinking-while-moving concurrent mechanism
to accelerate the DRL learning process, and a spatialchannel attention
mechanism to extract DNN feature maps of secondary importance for workload
offloading. This approach improves energy efficiency and inference latency for
different DNN models under various edge-cloud network conditions. Experimental
results on different datasets show that DVFO reduces the average energy
consumption by 33% compared to state-of-the-art schemes. Moreover, DVFO
achieves up to 54% end-to-end inference latency reduction.

通过提出一个新的基于 DVFS 的边缘 - 云协同推理框架 DVFO 来共同优化边缘设备的 CPU、GPU 和内存频率以及特征映射的云服务器卸载，以改善不同 DNN 模型在不同边缘 - 云网络条件下的能源效率和推理延迟，并通过实验结果展示其比现有方案降低了 33% 的平均能耗和最高达 54% 的端到端推理延迟。