我们提出了一个针对德国医学自然语言处理的统计模型，该模型训练了命名实体识别（NER），作为开放的公开模型。这项工作是我们第一个Gernerm模型的精致继任者，我们的工作大大优于我们的工作。我们证明了结合多种技术的有效性，以通过在预审预测的深度语言模型（LM），单词平衡和神经机器翻译上转移学习的方式来实现实体识别绩效。由于开放的公共医疗实体识别模型在德国文本上的稀疏情况，这项工作为医疗NLP作为基线模型的德国研究社区提供了好处。由于我们的模型基于公共英语数据，因此提供了其权重，而无需法律限制使用和分发。示例代码和统计模型可在以下网址获得：https：//github.com/frankkramer-lab/gernermed-pp

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data \& models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study will be available at https://github.com/LAION-AI/scaling-laws-openclip

Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around \textit{task vectors}. A task vector specifies a direction in the weight space of a pre-trained model, such that movement in that direction improves performance on the task. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition, and the behavior of the resulting model is steered accordingly. Negating a task vector decreases performance on the target task, with little change in model behavior on control tasks. Moreover, adding task vectors together can improve performance on multiple tasks at once. Finally, when tasks are linked by an analogy relationship of the form ``A is to B as C is to D", combining task vectors from three of the tasks can improve performance on the fourth, even when no data from the fourth task is used for training. Overall, our experiments with several models, modalities and tasks show that task arithmetic is a simple, efficient and effective way of editing models.

The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

机器学习和特别是强化学习（RL）在帮助我们了解神经决策过程方面非常成功。但是，RL在理解其他神经过程中的作用，尤其是运动学习的探索程度要少得多。为了探索这种联系，我们研究了最近的深度RL方法与基于错误的学习神经科学中的主要运动学习框架相对应。可以使用镜面反转适应范式探测基于错误的学习，在该范式中，它产生了独特的定性预测，这些预测在人类中观察到。因此，我们在镜面逆向上测试了现代深度RL算法的三个主要家庭。令人惊讶的是，所有算法都无法模仿人类的行为，并且确实表现出与基于错误的学习预测的行为。为了填补这一空白，我们引入了一种新颖的深度RL算法：基于模型的确定性策略梯度（MB-DPG）。 MB-DPG通过明确依靠观察到的动作结果来从基于错误的学习中汲取灵感。我们在镜像和旋转扰动下显示MB-DPG捕获（人）基于错误的学习。接下来，我们以MB-DPG的形式展示了基于错误的学习，比基于复杂的ARM的到达任务的规范无模型算法更快，同时比基于模型的RL更适合（正向）模型错误。这些发现突出了当前的深度RL方法与人类电动机适应之间的差距，并提供了缩小这一差距的途径，从而促进了两个领域之间未来的有益相互作用。

宇宙学冲击波对于理解宇宙结构的形成至关重要。为了研究它们，科学家运行计算昂贵的高分辨率3D流体动力模拟。解释仿真结果是具有挑战性的，因为结果数据集很大，并且由于其复杂的形态和多个冲击战线相交，因此，冲击波表面很难分离和分类。我们介绍了一条新颖的管道，处女座，结合了身体动机，可伸缩性和概率的鲁棒性，以解决这一无监督的分类问题。为此，我们使用低级别矩阵近似值的内核主成分分析来贬低粒子的数据集并创建标记的子集。我们执行监督分类，以随机变分深内核学习恢复完整的数据分辨率。我们对三个具有不同复杂性的最先进数据集进行评估，并取得良好的结果。所提出的管道自动运行，只有几个超参数，并且在所有测试的数据集上表现良好。我们的结果对于大规模应用是有希望的，我们重点介绍了现在的科学工作。

在许多图像分类任务中，诸如夹子之类的开放式摄影模型具有高精度。但是，在某些设置中，他们的零拍摄性能远非最佳。我们研究模型修补程序，目的是提高对特定任务的准确性，而不会在表现已经足够的任务上降低准确性。为了实现这一目标，我们引入了油漆，这是一种修补方法，该方法在微调之前使用模型的权重与要修补的任务进行微调后的权重。在零机夹的性能差的九个任务上，油漆可将精度提高15至60个百分点，同时将ImageNet上的精度保留在零拍模型的一个百分点之内。油漆还允许在多个任务上修补单个模型，并通过模型刻度进行改进。此外，我们确定了广泛转移的案例，即使任务不相交，对一个任务进行修补也会提高其他任务的准确性。最后，我们研究了超出常见基准的应用程序，例如计数或减少印刷攻击对剪辑的影响。我们的发现表明，可以扩展一组任务集，开放式摄影模型可实现高精度，而无需从头开始重新训练它们。

Web爬行的数据集已在最近的图像文本模型（例如剪辑（对比语言图像预训练）或火烈鸟）中启用了非凡的概括功能，但是对数据集创建过程知之甚少。在这项工作中，我们介绍了六个可公开可用数据源的测试床 - YFCC，LAION，概念标题，机智，redcaps，shutterstock-，以调查预训练分布如何在剪辑中诱导稳健性。我们发现，预训练数据的性能在分布变化之间有很大的变化，没有单个数据源主导。此外，我们系统地研究了这些数据源之间的相互作用，发现组合多个来源并不一定会产生更好的模型，而是稀释了最佳个体数据源的鲁棒性。我们将经验发现与简单环境中的理论见解相辅相成，其中结合训练数据还会导致稳健性稀释。此外，我们的理论模型为LAION数据集中最近采用的基于夹的数据过滤技术的成功提供了候选解释。总体而言，我们的结果表明，仅仅从Web中收集大量数据并不是建立预训练数据集以进行鲁棒性概括的最有效方法，因此需要进一步研究数据集设计。