Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision making, where many well-studied tasks like behavior cloning, offline RL, inverse dynamics, and waypoint conditioning correspond to different sequence maskings over a sequence of states, actions, and returns. We introduce the FlexiBiT framework, which provides a unified way to specify models which can be trained on many different sequential decision making tasks. We show that a single FlexiBiT model is simultaneously capable of carrying out many tasks with performance similar to or better than specialized models. Additionally, we show that performance can be further improved by fine-tuning our general model on specific tasks of interest.

Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection ($\sim 2\times$ unknown recall) and known object detection ($\sim 10\%$ mAP). Our code will be made available upon publication at https://github.com/orrzohar/PROB.

Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims to understand an underexplored area of FMs: classical data tasks like cleaning and integration. As a proof-of-concept, we cast five data cleaning and integration tasks as prompting tasks and evaluate the performance of FMs on these tasks. We find that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks. We identify specific research challenges and opportunities that these models present, including challenges with private and domain specific data, and opportunities to make data management systems more accessible to non-experts. We make our code and experiments publicly available at: https://github.com/HazyResearch/fm_data_tasks.

在经典曲线图中，给定实值曲线图信号，其曲线图傅里叶变换通常被定义为信号和图表拉普拉斯的每个特征向量之间的内部产品。不幸的是，在矢量值图表信号的情况下，该定义在数学上没有数学上有效，然而，在最先进的图表学习建模和分析中是典型的操作数。因此，寻求向矢量值信号解码的广义转换，因此本文的主要目的是本文的主要目的。探索了几次尝试，并且还发现在邻接等级的分层水平下进行转换，有助于更容易提高信号的光谱特性。拟议的方法被引入为一个新工具，协助图表学习模型的诊断和分析行为。

DNA存储的概念最早是在1959年由谁分享关于在谈话“有足够的空间在底部”纳米技术他的远见理查德·费曼建议。后来，对20世纪的结束，在基于DNA分子的存储解决方案的兴趣是随着人类基因组计划这反过来又导致了测序和组装方法显著进步的结果。 DNA存储在成熟的磁和光存储解决方案中享有重大优势。相对于磁性溶液，DNA存储不需要电力供应，以保持数据的完整性和优于在密度和耐用性的存储解决方案。鉴于趋势成本DNA合成和测序的降低，现在承认，在未来10 - 15年DNA存储内可能会成为一个高度竞争的归档技术，可能以后的主要这样的技术。随着中说，基于DNA的存储系统的当前实施方式是非常有限，并且不完全优化解决表征合成和测序过程错误的独特图案。在这项工作中，我们提出了一个强大，高效且可扩展的解决方案，以实现基于DNA的存储系统。我们的方法其部署重建的字母基于通过合成和测序过程中产生的拷贝不完善群集上的序列深神经网络（DNN）。特制的纠错码（ECC）被用来在此过程中发生的错误的作战模式。由于我们的重建方法适于不完善簇，我们的方法允许使用一种快速，可扩展的伪聚类而不是克服了嘈杂DNA拷贝聚类处理时的瓶颈。我们的回旋和变压器块和使用真实数据统计仿照合成数据进行训练之间架构整合。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

背景：人类的思想是多式联的。然而，大多数行为研究依赖于百年历史措施，例如任务准确性和延迟。为了更好地了解人类行为和大脑功能，我们应该引入其他措施并分析各个方面的行为。然而，它在技术上复杂且昂贵地设计和实施记录多种措施的实验。要解决此问题，需要一个允许从人类行为同步多种措施的平台。方法：本文介绍了名为OpenSync的OpenSource平台，可用于在神经科学实验中同步多种措施。该平台有助于自动集成，同步和记录生理测量（例如，脑电图（EEG），电流性皮肤响应（GSR），眼睛跟踪，身体运动等），用户输入响应（例如，来自鼠标，键盘，操纵杆等）和任务相关信息（刺激标记）。在本文中，我们解释了Opensync的结构和细节，提供了两种在精神病和团结的案例研究。与现有工具的比较：与专有系统（例如，审核）不同，OpenSync是免费的，它可以在任何替换实验设计软件（例如，Fleare，Openseame，Unity等，https://pypi.org/project/中使用OpenSync /和https://github.com/moeinrazavi/opensync_unity）。结果：我们的实验结果表明，OpenSync平台能够使用微秒分辨率同步多种措施。