超声使用是因为其成本低，非电离和非侵入性特征，并且已成为基石放射学检查。超声应用程序的研究也扩大了，尤其是通过机器学习的图像分析。但是，超声数据通常仅限于封闭的数据集，只有少数几个公开可用。尽管经常检查器官，但肾脏缺乏公开可用的超声数据集。拟议的开放肾脏超声数据集是第一套公开可用的肾脏B模式超声数据，其中包括用于多级语义分段的注释。它基于5年以上500多名患者的回顾性收集的数据，平均年龄为53.2 +/- 14。7年，体重指数为27.0 +/- 5.4 kg/m2，最常见的原发性疾病是糖尿病，IgA肾病和高血压。有两位专家超声师的视图标签和细粒度的手动注释。值得注意的是，该数据包括天然和移植的肾脏。进行了初始的基准测量测量，证明了一种最先进的算法，该算法达到了肾脏胶囊的骰子Sorenson系数为0.74。该数据集是一个高质量的数据集，包括两组专家注释，图像比以前可用的更大。为了增加获得肾脏超声数据的访问，未来的研究人员可能能够创建用于组织表征，疾病检测和预后的新型图像分析技术。

建模超声斑点对其表征组织特性的能力引起了极大的兴趣。由于斑点取决于潜在的组织结构，因此对其进行建模可能有助于分割或疾病检测等任务。但是，对于通常用于研究功能障碍的移植肾脏，目前尚不清楚哪个统计分布最能表征这种斑点。对于移植肾脏的区域而言，尤其如此：皮质，髓质和中央回声复合物。此外，目前尚不清楚这些分布如何因患者变量（例如年龄，性别，体重指数，原发性疾病或供体类型）而有所不同。这些特征可能会影响斑点建模，鉴于它们对肾脏解剖结构的影响。我们是第一个调查这两个目标的人。 n = 821肾移植受者B模式图像自动使用神经网络自动分段到皮质，髓质和中央回声复合物中。每个区域都安装了七个不同的概率分布。雷利和中族分布的模型参数在这三个区域之间有显着差异（p <= 0.05）。虽然两者都具有极好的合身性，但中田族具有更高的Kullbeck-Leibler Divergence。受体年龄与皮质中的尺度弱相关（Omega：Rho = 0.11，p = 0.004），而体重指数与髓质中的形状微弱相关（M：RHO = 0.08，p = 0.04）。性别，原发性疾病和供体类型均未表现出任何相关性。我们提出，根据我们的发现，中纳卡米分布可用于表征区域性的移植肾脏和大多数患者特征。

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time-varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time-varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.

As demand for large corpora increases with the size of current state-of-the-art language models, using web data as the main part of the pre-training corpus for these models has become a ubiquitous practice. This, in turn, has introduced an important challenge for NLP practitioners, as they are now confronted with the task of developing highly optimized models and pipelines for pre-processing large quantities of textual data, which implies, effectively classifying and filtering multilingual, heterogeneous and noisy data, at web scale. One of the main components of this pre-processing step for the pre-training corpora of large language models, is the removal of adult and harmful content. In this paper we explore different methods for detecting adult and harmful of content in multilingual heterogeneous web data. We first show how traditional methods in harmful content detection, that seemingly perform quite well in small and specialized datasets quickly break down when confronted with heterogeneous noisy web data. We then resort to using a perplexity based approach but with a twist: Instead of using a so-called "clean" corpus to train a small language model and then use perplexity so select the documents with low perplexity, i.e., the documents that resemble this so-called "clean" corpus the most. We train solely with adult and harmful textual data, and then select the documents having a perplexity value above a given threshold. This approach will virtually cluster our documents into two distinct groups, which will greatly facilitate the choice of the threshold for the perplexity and will also allow us to obtain higher precision than with the traditional classification methods for detecting adult and harmful content.

Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots' behavior. We approach the problem of inaccuracy during task allocation by modeling and controlling the overall ensemble behavior. Our model represents the allocation problem as a stochastic jump process and we regulate the mean and variance of such a process. The main contributions of this paper are: Establishing a structure for the transition rates of the equivalent stochastic jump process and formally showing that this approach leads to decoupled parameters that allow us to adjust the first- and second-order moments of the ensemble distribution over tasks, which gives the flexibility to decrease the variance in the desired final distribution. This allows us to directly shape the impact of uncertainties on the group allocation over tasks. We introduce a detailed procedure to design the gains to achieve the desired mean and show how the additional parameters impact the covariance matrix, which is directly associated with the degree of task allocation precision. Our simulation and experimental results illustrate the successful control of several robot ensembles during task allocation.

Scaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors? In this paper, we analyze the intermediate training checkpoints of differently sized OPT models (Zhang et al.,2022)--from 125M to 175B parameters--on next-token prediction, sequence-level generation, and downstream tasks. We find that 1) at a given perplexity and independent of model sizes, a similar subset of training tokens see the most significant reduction in loss, with the rest stagnating or showing double-descent behavior; 2) early in training, all models learn to reduce the perplexity of grammatical sequences that contain hallucinations, with small models halting at this suboptimal distribution and larger ones eventually learning to assign these sequences lower probabilities; 3) perplexity is a strong predictor of in-context learning performance on 74 multiple-choice tasks from BIG-Bench, and this holds independent of the model size. Together, these results show that perplexity is more predictive of model behaviors than model size or training computation.

许多参与者批评深度强化学习（DRL）算法在解决各种具有挑战性的强化学习（RL）问题方面已经取得了尖端的表现，包括具有高维连续状态和动作空间的复杂控制任务。尽管有广泛报道的成功，但现有的DRL算法经常遭受无效的勘探问题的困扰，从而导致学习稳定性和表现有限。为了解决这一限制，最近提出了几种集成DRL算法，以增强探索和稳定学习过程。但是，许多现有的合奏算法旨在单独训练每个基础学习者，而无需明确控制训练有素的基础学习者之间的协作。在本文中，我们提出了一种新技术，以基于多步集成方法来培训基础学习者的合奏。新的多步培训技术使我们能够为集合DRL开发一种新的层次结构培训算法，该算法通过显式的Inter-Learner参数共享来促进学习中的协作。理论上对我们的新算法的设计进行了验证。该算法在经验上也显示出在多个基准RL问题上的表现优于几种尖端的DRL算法。

随着人口的指数增长，至关重要的是保存自然资源，而不必损害足够的食物来养活所有人。这样做可以改善目前和后代的人的生计，健康和生态系统。可持续发展是联合国的范式，植根于食品，农作物，牲畜，森林，人口，甚至气体的排放。通过了解过去不同国家自然资源的总体使用，可以预测每个国家的需求。提出的解决方案包括使用统计回归模型实施机器学习系统，该模型可以预测将来在特定时期内每个国家 /地区短缺的顶级K产品。根据绝对误差和根平方误差的预测性能由于其低误差而显示出令人鼓舞的结果。该解决方案可以帮助组织和制造商了解满足全球需求所需的生产力和可持续性。

医学互联网是最近在医学方面的技术进步，对提供对健康指标的实时监控非常有帮助。本文提出了一种无创的物联网系统，该系统跟踪患者的情绪，尤其是患有自闭症谱系障碍的情绪。通过一些负担得起的传感器和云计算服务，对个人的心率进行监测和分析，以研究不同情绪每分钟汗水和心跳的变化的影响。在个人的正常休息条件下，建议的系统可以使用机器学习算法检测正确的情绪，其精度最高为92％。拟议方法的结果与医学物联网中最先进的解决方案相当。