Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

肾细胞癌（RCC）是一种常见的癌症，随着临床行为的变化。懒惰的RCC通常是低级的，没有坏死，可以在没有治疗的情况下监测。激进的RCC通常是高级的，如果未及时检测和治疗，可能会导致转移和死亡。虽然大多数肾脏癌在CT扫描中都检测到，但分级是基于侵入性活检或手术的组织学。确定对CT图像的侵略性在临床上很重要，因为它促进了风险分层和治疗计划。这项研究旨在使用机器学习方法来识别与病理学特征相关的放射学特征，以促进评估CT图像而不是组织学上的癌症侵略性。本文提出了一种新型的自动化方法，即按区域（Corrfabr）相关的特征聚集，用于通过利用放射学和相应的不对齐病理学图像之间的相关性来对透明细胞RCC进行分类。 CORRFABR由三个主要步骤组成：（1）特征聚集，其中从放射学和病理图像中提取区域级特征，（2）融合，放射学特征与病理特征相关的放射学特征在区域级别上学习，并且（3）在其中预测的地方学到的相关特征用于仅使用CT作为输入来区分侵略性和顽固的透明细胞RCC。因此，在训练过程中，Corrfabr从放射学和病理学图像中学习，但是在没有病理图像的情况下，Corrfabr将使用CORFABR将侵略性与顽固的透明细胞RCC区分开。 Corrfabr仅比放射学特征改善了分类性能，二进制分类F1分数从0.68（0.04）增加到0.73（0.03）。这证明了将病理疾病特征纳入CT图像上透明细胞RCC侵袭性的分类的潜力。

前列腺活检和图像引导的治疗程序通常是在与磁共振图像（MRI）的超声指导下进行的。准确的图像融合依赖于超声图像上前列腺的准确分割。然而，超声图像中降低的信噪比和工件（例如，斑点和阴影）限制了自动前列腺分割技术的性能，并将这些方法推广到新的图像域是本质上很难的。在这项研究中，我们通过引入一种新型的2.5D深神经网络来解决这些挑战，用于超声图像上的前列腺分割。我们的方法通过组合有监督的域适应技术和知识蒸馏损失，解决了转移学习和填充方法的局限性（即，在更新模型权重时，在更新模型权重时的性能下降）。知识蒸馏损失允许保留先前学习的知识，并在新数据集上的模型填充后降低性能下降。此外，我们的方法依赖于注意模块，该模块认为模型特征定位信息以提高分割精度。我们对一个机构的764名受试者进行了培训，并仅使用后续机构中的十个受试者对我们的模型进行了审核。我们分析了方法在三个大型数据集上的性能，其中包括来自三个不同机构的2067名受试者。我们的方法达到了平均骰子相似性系数（骰子）为$ 94.0 \ pm0.03 $，而Hausdorff距离（HD95）为2.28 $ mm $，在第一机构的独立受试者中。此外，我们的模型在其他两个机构的研究中都很好地概括了（骰子：$ 91.0 \ pm0.03 $; hd95：3.7 $ mm $ and Dice：$ 82.0 \ pm0.03 $; hd95 $; hd95：7.1 $ mm $）。

前列腺癌是美国男人的第二致致命癌症。虽然磁共振成像（MRI）越来越多地用于引导前列腺癌诊断的靶向活组织检查，但其效用仍然受到限制，因为假阳性和假否定的高率以及较低的读者协议。机器学习方法在前列腺MRI上检测和定位癌症可以帮助标准化放射科学诠释。然而，现有的机器学习方法不仅在模型架构中不等，而且还可以在用于模型培训的地面真理标签策略中。在这项研究中，我们比较不同的标记策略，即病理证实放射科标签，整个安装组织病理学图像上的病理学家标签，以及病变水平和像素级数字病理学家标签（先前验证了组织病理学图像上的深层学习算法以预测像素 - 整个安装组织病理学图像上的Gleason模式）。我们分析这些标签对训练有素的机器学习模型的性能的影响。我们的实验表明，用它们培训的（1）放射科标签和模型可能会错过癌症，或低估癌症程度，（2）与他们培训的数字病理学家标签和模型与病理学家标签有高度的一致性，而（3）用数字病理学家培训的模型标签在两种不同疾病分布的两种不同群组中达到最佳性能，而不管使用的模型建筑如何。数字病理学家标签可以减少与人类注释相关的挑战，包括劳动力，时间，和读者间变异性，并且可以通过使可靠的机器学习模型进行培训来检测和定位前列腺癌，帮助弥合前列腺放射学和病理学之间的差距在MRI。

When answering natural language questions over knowledge bases (KBs), incompleteness in the KB can naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We first identify various forms of KB incompleteness that can result in a question being unanswerable. We then propose GrailQAbility, a new benchmark dataset, which systematically modifies GrailQA (a popular KBQA dataset) to represent all these incompleteness issues. Testing two state-of-the-art KBQA models (trained on original GrailQA as well as our GrailQAbility), we find that both models struggle to detect unanswerable questions, or sometimes detect them for the wrong reasons. Consequently, both models suffer significant loss in performance, underscoring the need for further research in making KBQA systems robust to unanswerability.

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

With the steady emergence of community question answering (CQA) platforms like Quora, StackExchange, and WikiHow, users now have an unprecedented access to information on various kind of queries and tasks. Moreover, the rapid proliferation and localization of these platforms spanning geographic and linguistic boundaries offer a unique opportunity to study the task requirements and preferences of users in different socio-linguistic groups. In this study, we implement an entity-embedding model trained on a large longitudinal dataset of multi-lingual and task-oriented question-answer pairs to uncover and quantify the (i) prevalence and distribution of various online tasks across linguistic communities, and (ii) emerging and receding trends in task popularity over time in these communities. Our results show that there exists substantial variance in task preference as well as popularity trends across linguistic communities on the platform. Findings from this study will help Q&A platforms better curate and personalize content for non-English users, while also offering valuable insights to businesses looking to target non-English speaking communities online.

We derive a learning framework to generate routing/pickup policies for a fleet of vehicles tasked with servicing stochastically appearing requests on a city map. We focus on policies that 1) give rise to coordination amongst the vehicles, thereby reducing wait times for servicing requests, 2) are non-myopic, considering a-priori unknown potential future requests, and 3) can adapt to changes in the underlying demand distribution. Specifically, we are interested in adapting to fluctuations of actual demand conditions in urban environments, such as on-peak vs. off-peak hours. We achieve this through a combination of (i) online play, a lookahead optimization method that improves the performance of rollout methods via an approximate policy iteration step, and (ii) an offline approximation scheme that allows for adapting to changes in the underlying demand model. In particular, we achieve adaptivity of our learned policy to different demand distributions by quantifying a region of validity using the q-valid radius of a Wasserstein Ambiguity Set. We propose a mechanism for switching the originally trained offline approximation when the current demand is outside the original validity region. In this case, we propose to use an offline architecture, trained on a historical demand model that is closer to the current demand in terms of Wasserstein distance. We learn routing and pickup policies over real taxicab requests in downtown San Francisco with high variability between on-peak and off-peak hours, demonstrating the ability of our method to adapt to real fluctuation in demand distributions. Our numerical results demonstrate that our method outperforms rollout-based reinforcement learning, as well as several benchmarks based on classical methods from the field of operations research.

近年来，地标复合物已成功地用于无定位和无公制的自主探索，并使用一组受GPS污染的环境中的一组感应有限的限制和沟通有限的机器人。为了确保快速而完整的探索，现有的作品对环境中地标的密度和分布做出了假设。这些假设可能过于限制，尤其是在可能被破坏或完全缺失的危险环境中。在本文中，我们首先提出了一个深入的加强学习框架，用于在具有稀疏地标的环境中，同时减少客户服务器交流的环境中的多代理合作探索。通过利用有关部分可观察性和信用分配的最新发展，我们的框架可以为多机器人系统有效地培训勘探政策。该政策从范围和分辨率有限的接近传感器基于近距离传感器的行动中获得个人奖励，该传感器与小组奖励相结合，以鼓励通过观察0-，1-维度和2维的简单来鼓励地标综合体的协作探索和建设。此外，我们采用三阶段的课程学习策略来通过逐渐增加随机障碍并破坏随机地标来减轻奖励稀疏性。模拟中的实验表明，我们的方法在不同环境之间具有稀疏地标的效率中的最先进的地标复杂探索方法。

在本文中，我们引入了一种半居中的控制技术，用于在不确定的遮挡环境中运送脆弱物体到目的地的一群机器人。建议的方法已分为两部分。初始部分（第1阶段）包括一种集中的控制策略，用于在代理之间创建特定的形成，以便可以将要运输的对象正确放在系统顶部。我们提出了一种与基于圆形区域的形状控制方法融合在一起的新型三角填料方案，用于在机器人之间创建刚性配置。在后面的部分（第2阶段），需要群体系统以采用基于区域的形状控制方法的分散方式将对象传达到目的地。模拟结果以及比较研究证明了我们提出的方案的有效性。