为了了解材料特性的起源，三轴光谱仪（TAS）处的中子散射实验通过测量其动量（Q）和能量（E）空间中的强度分布来研究样品中的磁和晶格激发。但是，TAS实验的高需求和有限的光束时间可用性提出了自然的问题，即我们是否可以提高其效率或更好地利用实验者的时间。实际上，使用TAS，有许多科学问题需要在Q-E空间的特定区域中搜索感兴趣的信号，但是当手动完成时，这是耗时且效率低下的，因为测量点可能会放置在此类的无信息区域中作为背景。主动学习是一种有前途的通用机器学习方法，可以迭代地检测自主信号的信息区域，即不受人类干扰，从而避免了不必要的测量并加快实验。此外，自主模式允许实验者在此期间专注于其他相关任务。我们在本文中描述的方法利用了对数高斯过程，由于对数转换，该过程在信号区域中具有最大的近似不确定性。因此，将不确定性最大化为采集功能，因此直接产生了信息测量的位置。我们证明了我们方法对在Themal Tas Eiger（PSI）进行真实中子实验的结果的好处，以及在合成环境中基准的结果，包括许多不同的激发。

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.

Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).

We show how the inherent, but often neglected, properties of large-scale LiDAR point clouds can be exploited for effective self-supervised representation learning. To this end, we design a highly data-efficient feature pre-training backbone that significantly reduces the amount of tedious 3D annotations to train state-of-the-art object detectors. In particular, we propose a Masked AutoEncoder (MAELi) that intuitively utilizes the sparsity of the LiDAR point clouds in both, the encoder and the decoder, during reconstruction. This results in more expressive and useful features, directly applicable to downstream perception tasks, such as 3D object detection for autonomous driving. In a novel reconstruction scheme, MAELi distinguishes between free and occluded space and leverages a new masking strategy which targets the LiDAR's inherent spherical projection. To demonstrate the potential of MAELi, we pre-train one of the most widespread 3D backbones, in an end-to-end fashion and show the merit of our fully unsupervised pre-trained features on several 3D object detection architectures. Given only a tiny fraction of labeled frames to fine-tune such detectors, we achieve significant performance improvements. For example, with only $\sim800$ labeled frames, MAELi features improve a SECOND model by +10.09APH/LEVEL 2 on Waymo Vehicles.

Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, prohibited by the lack of text-lidar datasets. In this work, we propose LidarCLIP, a mapping from automotive point clouds to a pre-existing CLIP embedding space. Using image-lidar pairs, we supervise a point cloud encoder with the image CLIP embeddings, effectively relating text and lidar data with the image domain as an intermediary. We show the effectiveness of LidarCLIP by demonstrating that lidar-based retrieval is generally on par with image-based retrieval, but with complementary strengths and weaknesses. By combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios under adverse sensor conditions. We also use LidarCLIP as a tool to investigate fundamental lidar capabilities through natural language. Finally, we leverage our compatibility with CLIP to explore a range of applications, such as point cloud captioning and lidar-to-image generation, without any additional training. We hope LidarCLIP can inspire future work to dive deeper into connections between text and point cloud understanding. Code and trained models available at https://github.com/atonderski/lidarclip.

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how -- by exploiting the (potentially) partial cardinal and partial probabilistic information -- more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such operations efficiently, analog in-memory computing platforms based on emerging devices, e.g., resistive RAM (RRAM), have been introduced. These acceleration platforms rely on analog properties of the devices and thus suffer from process variations and noise. Consequently, weights in neural networks configured into these platforms can deviate from the expected values, which may lead to feature errors and a significant degradation of inference accuracy. To address this issue, in this paper, we propose a framework to enhance the robustness of neural networks under variations and noise. First, a modified Lipschitz constant regularization is proposed during neural network training to suppress the amplification of errors propagated through network layers. Afterwards, error compensation is introduced at necessary locations determined by reinforcement learning to rescue the feature maps with remaining errors. Experimental results demonstrate that inference accuracy of neural networks can be recovered from as low as 1.69% under variations and noise back to more than 95% of their original accuracy, while the training and hardware cost are negligible.

Originating from cooperative game theory, Shapley values have become one of the most widely used measures for variable importance in applied Machine Learning. However, the statistical understanding of Shapley values is still limited. In this paper, we take a nonparametric (or smoothing) perspective by introducing Shapley curves as a local measure of variable importance. We propose two estimation strategies and derive the consistency and asymptotic normality both under independence and dependence among the features. This allows us to construct confidence intervals and conduct inference on the estimated Shapley curves. The asymptotic results are validated in extensive experiments. In an empirical application, we analyze which attributes drive the prices of vehicles.

In this paper, we elaborate on the design and discuss the results of a multi-agent simulation that we have developed using the PSI cognitive architecture. We demonstrate that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents. This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents. Our simulation also shows how parameterization can have dramatic effects on agent behavior. Introducing an out-group bias, for example, not only made agents behave aggressively toward members of the other group, but it also increased in-group cohesion. Similarly, environmental and situational factors facilitated the emergence of outliers: agents from adversarial groups becoming close friends. Overall, this simulation showcases the power of psychological frameworks, in general, and the PSI paradigm, in particular, to bring about human-like behavioral patterns in an emergent fashion.

线性神经网络层的模棱两可。在这项工作中，我们放宽了肩variance条件，只有在投影范围内才是真实的。特别是，我们研究了投射性和普通的肩那样的关系，并表明对于重要的例子，这些问题实际上是等效的。3D中的旋转组在投影平面上投影起作用。在设计用于过滤2D-2D对应的网络时，我们在实验上研究了旋转肩位的实际重要性。完全模型的模型表现不佳，虽然简单地增加了不变的特征，从而在强大的基线产量中得到了改善，但这似乎并不是由于改善的均衡性。