The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Millions of people participate in online peer-to-peer support sessions, yet there has been little prior research on systematic psychology-based evaluations of fine-grained peer-counselor behavior in relation to client satisfaction. This paper seeks to bridge this gap by mapping peer-counselor chat-messages to motivational interviewing (MI) techniques. We annotate 14,797 utterances from 734 chat conversations using 17 MI techniques and introduce four new interviewing codes such as chit-chat and inappropriate to account for the unique conversational patterns observed on online platforms. We automate the process of labeling peer-counselor responses to MI techniques by fine-tuning large domain-specific language models and then use these automated measures to investigate the behavior of the peer counselors via correlational studies. Specifically, we study the impact of MI techniques on the conversation ratings to investigate the techniques that predict clients' satisfaction with their counseling sessions. When counselors use techniques such as reflection and affirmation, clients are more satisfied. Examining volunteer counselors' change in usage of techniques suggest that counselors learn to use more introduction and open questions as they gain experience. This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.

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.

Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particular, we propose a method for partially observable reinforcement learning that uses a fully observable policy (which we call a state expert) during offline training to improve online performance. Based on Soft Actor-Critic (SAC), our agent balances performing actions similar to the state expert and getting high returns under partial observability. Our approach can leverage the fully-observable policy for exploration and parts of the domain that are fully observable while still being able to learn under partial observability. On six robotics domains, our method outperforms pure imitation, pure reinforcement learning, the sequential or parallel combination of both types, and a recent state-of-the-art method in the same setting. A successful policy transfer to a physical robot in a manipulation task from pixels shows our approach's practicality in learning interesting policies under partial observability.

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

与LTE网络相比，5G的愿景在于提供较高的数据速率，低延迟（为了实现近实时应用程序），大大增加了基站容量以及用户的接近完美服务质量（QoS）。为了提供此类服务，5G系统将支持LTE，NR，NR-U和Wi-Fi等访问技术的各种组合。每种无线电访问技术（RAT）都提供不同类型的访问，这些访问应在用户中对其进行最佳分配和管理。除了资源管理外，5G系统还将支持双重连接服务。因此，网络的编排对于系统经理在旧式访问技术方面来说是一个更困难的问题。在本文中，我们提出了一种基于联合元学习（FML）的大鼠分配算法，该算法使RAN Intelligent Controller（RIC）能够更快地适应动态变化的环境。我们设计了一个包含LTE和5G NR服务技术的模拟环境。在模拟中，我们的目标是在传输的截止日期内满足UE需求，以提供更高的QoS值。我们将提出的算法与单个RL试剂，爬行动物算法和基于规则的启发式方法进行了比较。仿真结果表明，提出的FML方法分别在第一部部署回合21％和12％时达到了较高的缓存率。此外，在比较方法中，提出的方法最快地适应了新任务和环境。

引入了一种新的多模式传感器融合方法。该技术依赖于两个阶段的过程。在第一阶段，由未标记的训练数据构建了多模式生成模型。在第二阶段，生成模型是先验的重建和传感器融合任务的搜索歧管。该方法还处理仅通过亚采样即可（即压缩传感）访问观测值的情况。我们在一系列多模式融合实验（例如多感官分类，降解和从子采样观测值中恢复）上展示了有效性和出色的性能。

事件摄像机是受到生物启发的动态视觉传感器，它们以高时间分辨率，高动态范围和低延迟响应图像强度的变化。这些传感器特性非常适合与智能视觉信标的广播视觉通信频道一起启用视觉目标跟踪，并在分布式机器人技术中应用。视觉信标可以通过对发射二极管（LED）的高频调节（例如车辆前大灯，物联网（IoT）LED，智能建筑灯等）来构建，这些灯光已经存在于许多真实世界中。事件摄像机的高时间分辨率特征使他们能够以基于经典的框架摄像机的速度捕获更高数据速率的视觉信号。在本文中，我们提出了一种具有LED调制和事件摄像头解调算法的新型智能视觉标准架构。我们定量评估我们原型型的智能视觉信标通信系统的LED传输速率，通信距离和消息传输精度之间的关系。所提出的方法在室内环境中最多可实现4 kbps，并且在100米的距离内以500桶的传输速率在阳光下以500 bps的速度实现了无损的传播，这表明了该技术在室外环境中的潜力。

变形金刚在序列建模及以后取得了显着的成功，但相对于输入序列的长度，二次计算和记忆复杂性遭受了损失。利用技术包括稀疏和线性的注意力和哈希技巧；已经提出了有效的变压器来降低变压器的二次复杂性，但会显着降低准确性。作为响应，我们首先将计算注意图的线性注意力和残差连接解释为梯度下降步骤。然后，我们将动量引入这些组件，并提出\ emph {动量变压器}，该动量利用动量来提高线性变压器的精度，同时保持线性内存和计算复杂性。此外，我们制定了一种自适应策略，以根据二次优化的最佳动量计算模型的动量值。这种自适应动量消除了寻找最佳动量值的需求，并进一步增强了动量变压器的性能。包括图像生成和机器翻译在内的自回归和非自动回归任务的一系列实验表明，动量变压器在训练效率和准确性方面优于流行的线性变压器。

Boll Weevil（Anthonomus Grandis L.）是一种严重的害虫，主要以棉花为食。由于亚热带气候条件，在德克萨斯州的下里奥格兰德山谷等地方，棉花植物可以全年生长，因此，收获期间上一个季节的剩下的种子可以在玉米中的旋转中继续生长（Zea Mays L.）和高粱（高粱双色L.）。这些野性或志愿棉花（VC）植物到达Pinhead平方阶段（5-6叶阶段）可以充当Boll Weevil Pest的宿主。得克萨斯州的鲍尔象鼻虫根除计划（TBWEP）雇用人们在道路或田野侧面生长的风险投资和消除旋转作物的田间生长，但在田野中生长的植物仍未被发现。在本文中，我们证明了基于您的计算机视觉（CV）算法的应用，仅在三个不同的生长阶段（V3，V6）（V3，V6）中检测出在玉米场中生长的VC植物，以检测在玉米场中生长的VC植物的应用。使用无人飞机系统（UAS）遥感图像。使用Yolov5（S，M，L和X）的所有四个变体，并根据分类精度，平均平均精度（MAP）和F1得分进行比较。发现Yolov5s可以在玉米的V6阶段检测到最大分类精度为98％，地图为96.3％，而Yolov5s和Yolov5m的地图为96.3％，而Yolov5m的分类精度为85％，Yolov5m和Yolov5m的分类准确性最小，而Yolov5L的分类精度最少。在VT阶段，在尺寸416 x 416像素的图像上为86.5％。开发的CV算法有可能有效地检测和定位在玉米场中间生长的VC植物，并加快TBWEP的管理方面。