1. Explore In-Context Learning for 3D Point Cloud Understanding 

   Fang, Zhongbin; Li, Xiangtai; Li, Xia; et al. (2023)

   Advances in Neural Information Processing Systems 36

   With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language processing and computer vision tasks. Meanwhile, in-context learning is still largely unexplored in the 3D point cloud domain. Although masked modeling has been successfully applied for in-context learning in 2D vision, directly extending it to 3D point clouds ...

   Conference Paper

2. Efficient Exploration in Continuous-time Model-based Reinforcement Learning 

   Treven, Lenart; Hübotter, Jonas; Sukhija, Bhavya; et al. (2023)

   Advances in Neural Information Processing Systems 36

   Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time. In this paper, we introduce a model-based reinforcement learning algorithm that represents continuous-time dynamics using nonlinear ordinary differential equations (ODEs). We capture epistemic uncertainty using well-calibrated probabilistic models, and use the optimistic principle for exploration. ...

   Conference Paper

3. Non-commutative perturbation theory for spin dynamics explains the factorization properties of RIDME background 

   Kuzin, Sergei; Yulikov, Maxim; Jeschke, Gunnar (2024)

   Journal of Magnetic Resonance

   The intermolecular hyperfine relaxation-induced dipolar modulation enhancement (ih-RIDME) experiment has a promising potential to quantitatively characterize the nuclear environment in the 0.8-3 nm range around an electron spin. Such information about the spatial arrangement of nuclei is of great interest for structural biology as well as for dynamic nuclear polarization (DNP) methods. In order to develop a reliable and sensitive spectroscopic ...

   Journal Article

4. Effects of using nanosecond repetitively pulsed discharge and turbulent jet ignition on internal combustion engine performance 

   Balmelli, Michelangelo; Hilfiker, Thomas; Biela, Jürgen; et al. (2024)

   Energy Conversion and Management

   Robust ignition of hard-to-ignite fuels is essential for future spark ignited internal combustion engines, particularly for introducing efficiency-enhancing diesel-like process parameters like air excess or high amounts of exhaust gas recirculation (EGR). On the one hand, novel plasma-based ignition systems like Nanosecond Repetitively Pulsed Discharge (NRPD) are promising in extending the ignition limits and the early flame development ...

   Journal Article

5. CLadder: Assessing Causal Reasoning in Language Models 

   Jin, Zhijing; Chen, Yuen; Leeb, Felix; et al.

   Advances in Neural Information Processing Systems 36

   The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules. ...

   Conference Paper

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