To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

As large language models gain widespread adoption, running them efficiently becomesacrucialtask.

Results for 3DMatch are shown in Table 1: the performance gain achieved by Compass when deploying theproposed data augmentation validates itsimportance. Indeed, without theproposed augmentation FLARE performs better than Compass on this dataset. This dataset has been specifically proposed to verify the invariance to rotations of the learned 3D descriptors [1], and containsonlyatestsplit. In Figure 2, we consider two pairs of local surface patches and their corresponding feature maps: both patches forming a pair are extracted around the same keypoint on different fragments. The canonical pose computed for the first pair is repeatable, while the second pair represents a failure ofCompass.

This task is commonly addressed by handcrafted algorithms exploiting geometric cues deemed as distinctive and robust by the designer. Yet, one might conjecture that humans learn the notion oftheinherent orientation of3Dobjectsfromexperience andthatmachines may do so alike. In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds.

Computational simulations have revolutionized materials design, accelerating innovation by allowing researchers to explore material properties and their behaviors virtually before experimental validation[1-4]. This shift has led to significant breakthroughs that range from energy storage[5, 6] to pharmaceutical development[7, 8]. However, a persistent challenge undermines this potential: the technical barriers to effective simulation setup disproportionately burden researchers, particularly those whose expertise lies in experimental rather than computational domains. When scientists identify a promising new compound, understanding its fundamental properties often requires computational validation. Y et, even seemingly straightforward simulations frequently lead to lengthy technical challenges. Even experienced computational scientists (physicists, chemists, engineers) find themselves diverted from scientific inquiry toward navigating complex programming challenges, engaging in trial-and-error attempts, and struggling with computational setup details rather than focusing on the scientific questions[9]. Integrated Computational Materials Engineering (ICME) has emerged as a robust framework to accelerate materials development by synergizing experimental data, simulations, and theoretical models across multiple scales.

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network.

Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introducing small faults, or mutants, into the code. However, traditional mutation operators are poorly suited to robotic programs, which involve message-based commands and interactions with the physical world. This paper explores the adaptation of mutation testing to IRS by defining domain-specific mutation operators that capture the semantics of robot actions and sensor readings. We propose a methodology for generating meaningful mutants at the level of high-level read and write operations, including movement, gripper actions, and sensor noise injection. An empirical study on a pick-and-place scenario demonstrates that our approach produces more informative mutants and reduces the number of invalid or equivalent cases compared to conventional operators. Results highlight the potential of mutation testing to enhance test suite quality and contribute to safer, more reliable industrial robotic systems.

A formal link between regression and classification has been tenuous. Even though the margin maximization term $\|w\|$ is used in support vector regression, it has at best been justified as a regularizer. We show that a regression problem with $M$ samples lying on a hyperplane has a one-to-one equivalence with a linearly separable classification task with $2M$ samples. We show that margin maximization on the equivalent classification task leads to a different regression formulation than traditionally used. Using the equivalence, we demonstrate a ``regressability'' measure, that can be used to estimate the difficulty of regressing a dataset, without needing to first learn a model for it. We use the equivalence to train neural networks to learn a linearizing map, that transforms input variables into a space where a linear regressor is adequate.