The perception of an Autonomous Driving System (ADS) critically depends on relevant, comprehensive, and diverse datasets to ensure its safety while operating in the environment. Field data collection lacks completeness with respect to the list of rare but still possible safety-related scenarios needed for the development, verification, and validation of the ADS. 3D Gaussian Splatting (3DGS) has shown promising capabilities for the reconstruction and editing of scenes based on data collected by cameras and LiDAR sensors. However, the industrial fidelity evaluation of reconstructions is underexplored, which is crucial when employing such methods in safety-related systems, especially for ADS. This becomes more challenging as ADS operates in a dynamic, uncontrolled environment with limited viewpoints and often partially occluded objects. This paper addresses this gap by proposing and implementing a framework (Fig. 1) to systematically analyze the capabilities and limitations of 3DGS for use in the reconstruction of safety-related scenes. It focuses on the quality of reconstruction for vehicles and pedestrians, which are the two most critical object classes for ADS. Our findings provide
Only a small fraction of the data generated in state-of-the-art all-atom multi-microsecond molecular dynamics (MD) simulations is typically analyzed. With femtosecond integration steps, microsecond simulations generate billions of time steps containing atomic positions, velocities, and forces, often corresponding to petabytes of data. Since this exceeds practical storage capacities, only a subset of frames is usually written to trajectory files at intervals of 10-100 ps. While sufficient for ensemble averages and slow dynamics, this approach discards information on fast processes such as molecular vibrations, solvent dynamics, short-lived transition states, and transport phenomena that are directly related to macroscopic properties and experimental observables. Here, we introduce a streaming framework for MD simulations that provides direct access to all data generated during a running simulation. Instead of writing high-frequency output to disk, the framework enables user-defined analysis and processing routines to access simulation data in real time. To achieve this, we extend the Interactive Molecular Dynamics (IMD) protocol and implement an enhanced version, termed IMDv3, in th
Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern data-driven approaches. The newer approaches, such as feed-forward reconstruction models, have benefited extensively from scaling dataset sizes, whereas local feature matching models are still only trained on a few mid-sized datasets. In this paper, we revisit local feature matching from a data-driven perspective. In our approach, which we call LoMa, we combine large and diverse data mixtures, modern training recipes, scaled model capacity, and scaled compute, resulting in remarkable gains in performance. Since current standard benchmarks mainly rely on collecting sparse views from successful 3D reconstructions, the evaluation of progress in feature matching has been limited to relatively easy image pairs. To address the resulting saturation of benchmarks, we collect 1000 highly challenging image pairs from internet data into a new dataset called HardMatch. Ground truth correspondences for HardMatch are obtained via manual annotation by the authors. In our extensive benchmarking suite, we find that LoMa ma
Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure o
Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, progress in the field is hindered by inconsistent evaluation practices: studies rely on oversimplified synthetic data generators, adopt incompatible metrics, and lack transparency in hyperparameter selection, making fair comparisons difficult. We address this gap with a novel benchmarking framework comprising three contributions: (1) a drift simulation method that injects controlled distributional changes into real-world datasets via Monte Carlo trials, enabling supervised evaluation while preserving real-world data complexity; (2) an evaluation protocol for drift detection with timing-aware criteria, including the derivation of new metrics (e.g., F1 detection score, normalized detection time) that are comparable across streams; and (3) we advocate for a leave-one-dataset-out hyperparameter optimization protocol for drift detection methods that promotes configuration robustness across heterogeneous stream dynamics. We benchmark 14 widely used drift detection methods on 7 realworld datasets across 4 drift t
Understanding protein conformational dynamics is essential for elucidating biological function but remains challenging due to the wide range of timescales and the complexity of collective motions. Enhanced sampling methods overcome timescale limitations of conventional molecular dynamics, yet their effectiveness depends on the choice of collective variables (CVs), which are often difficult to define and may lack physical interpretability. In particular, collective variables derived from machine learning or collective vibrational modes can efficiently capture slow dynamics but are not easily mapped onto intuitive structural descriptors. Here, we present a fully automated framework that transforms enhanced sampling trajectories into human-readable representations of protein dynamics. Our approach combines enhanced sampling along CVs derived from frequency-selective anharmonic mode analysis with a post hoc analysis of biased trajectories using weighted dynamic cross-correlation matrices. From these, we identify residue pairs and domains exhibiting correlated and anti-correlated motions, yielding simple domain-domain distances that serve as physically interpretable CVs. We apply this m
At room temperature, low frequency vibrations at far-infrared frequencies are thermally excited ($k_B T > h ν$) and not restricted to harmonic fluctuations around a single potential energy minimum. For folded proteins, these intrinsically anharmonic vibrations can contain information on slow conformational transitions. Recently, we have developed FREquency-SElective ANharmonic (FRESEAN) mode analysis, a method based on time correlation functions that isolates low-frequency vibrational motions from molecular dynamics simulation trajectories without relying on harmonic approximations. We recently showed that low-frequency vibrations obtained from FRESEAN mode analysis are effective collective variables in enhanced sampling simulations of conformational ensembles. However, FRESEAN mode analysis is based on velocity time correlations between all degrees of freedom, which creates computational challenges for large biomolecules. To facilitate future applications, we demonstrate here how coarse-graining of all-atom simulation trajectories can be combined with FRESEAN mode analysis to extract information on low-frequency vibrations at minimal computational cost.
In this paper, we speed up robust two-view relative pose from dense correspondences. Previous work has shown that dense matchers can significantly improve both accuracy and robustness in the resulting pose. However, the large number of matches comes with a significantly increased runtime during robust estimation in RANSAC. To avoid this, we propose an efficient match summarization scheme which provides comparable accuracy to using the full set of dense matches, while having 10-100x faster runtime. We validate our approach on standard benchmark datasets together with multiple state-of-the-art dense matchers.
Elastic MicroPhase Separation (EMPS) provides a simple route to create soft materials with homogeneous microstructures by leveraging the supersaturation of crosslinked polymer networks with liquids. At low supersaturation, network elasticity stabilizes a uniform mixture, but beyond a critical threshold, metastable microphase-separated domains emerge. While previous theories have focused on describing qualitative features about the size and morphology of these domains, they do not make quantitative predictions about EMPS phase diagrams. In this work, we extend Flory-Huggins theory to quantitatively capture EMPS phase diagrams by incorporating strain-stiffening effects. This model requires no fitting parameters and relies solely on independently measured solubility parameters and large-deformation mechanical responses. Our results reveal that strain-stiffening enables metastable microphase separation within the swelling equilibrium state and why the microstructures can range from discrete droplets to bicontinuous networks. This works highlights the critical role of nonlinear elasticity in controlling phase-separated morphologies in polymer gels.
This work targets the influence of disorder on the relaxed structure and macroscopic mechanical properties of elastic networks. We construct network classes of different types of disorder (length, topology and stiffness), which are subsequently equilibrated in a finite kinematics setting. Relaxed network structures are distinct among network classes, which opens the path towards exploiting easily accessible experimental measures as a way of inferring further microstructural details.
Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since metering data is beneficial for fault-free grid operation, power management, and resource allocation, applying privacy-preserving techniques to smart metering data is an important research problem. This work addresses this by using secure multi-party computation (SMPC), allowing multiple parties to jointly evaluate functions of their private inputs without revealing the latter. Concretely, we show how to perform power flow analysis on cryptographically hidden prosumer data. More precisely, we present a tailored solution to the power flow problem building on an SMPC implementation of Newtons method. We analyze the security of our approach in the universal composability framework and provide benchmarks for various grid types, threat models, and solvers. Our results indicate that secure multi-party computation can be able to alleviate
Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing that it is possible to classify breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three different methods: softmax, energy score and deep ensembles. All methods are tested on three different OOD data sets. The results show that the energy score method outperforms the softmax method, performing well on two of the data sets. The ensemble method is the most robust, performing the best at detecting OOD samples for all three OOD data sets.
Protein function does not solely depend on structure but often relies on dynamical transitions between distinct conformations. Despite this fact, our ability to characterize or predict protein dynamics is substantially less developed compared to state-of-the-art protein structure prediction. Molecular simulations provide unique opportunities to study protein dynamics, but the timescales associated with conformational changes generate substantial challenges. Enhanced sampling algorithms with collective variables can greatly reduce the computational cost of sampling slow processes. However, defining collective variables suitable to enhance sampling of protein conformational transitions is non-trivial. Low-frequency vibrations have long been considered as promising candidates for collective variable but their identification so far relied on assumptions inherently invalid at low frequencies. We recently introduced an analysis of molecular vibrations that does not rely on such approximations and remains accurate at low frequencies. Here, we modified this approach to efficiently isolate low-frequency vibrations in proteins and applied it to a set of five proteins of varying complexity. W
Hydrogels are particularly versatile materials that are widely found in both Nature and industry. One key reason for this versatility is their high water content, which lets them dramatically change their volume and many of their mechanical properties -- often by orders of magnitude -- as they swell and dry out. Currently, we lack techniques that can precisely characterize how these properties change with water content. To overcome this challenge, here we develop Gel-Freezing Osmometry (GelFrO): an extension of freezing-point osmometry. We show how GelFrO can measure a hydrogel's mechanical response to compression and osmotic pressure, while only using small, $O(100μ$L$)$ samples. Because the technique allows measurement of properties over an unusually wide range of water contents, it allows us to accurately test theoretical predictions. We find simple, power-law behavior for both mechanical response to compression, and osmotic pressure, while these are not well-captured by classical Flory-Huggins theory. We interpret this power-law behavior as a hallmark of a microscopic fractal structure of the gel's polymer network, and propose a simple way to connect the gel's fractal dimension
In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates into a classification problem, where knowledge from seen categories is transferred to unseen categories by exploiting the relationships between visual features and available semantic information, such as text corpora or manual annotations. However, learning this joint distribution is costly and requires one-to-one training with corresponding semantic information. We present a reversed conditional Diffusion-based model (RevCD) that mitigates this issue by generating semantic features synthesized from visual inputs by leveraging Diffusion models' conditional mechanisms. Our RevCD model consists of a cross Hadamard-Addition embedding of a sinusoidal time schedule and a multi-headed visual transformer for attention-guided embeddings. The proposed approach introduces three key innovations. First, we reverse the process of generating semantic space based on visual data, introducing a novel loss function that facilitates more efficient knowledge transfer. Second, we apply Diffusion models to zero-shot learning - a
We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on timescales faster than conformational transitions but describe a path of least resistance towards structural change. A key challenge is that harmonic approximations are ill-suited to characterize these vibrations, which are observed at far-infrared frequencies and are easily excited by thermal collisions at room temperature. Here, we approached this problem with a frequency-selective anharmonic (FRESEAN) mode analysis that does not rely on harmonic approximations and successfully isolates anharmonic low-frequency vibrations from short molecular dynamics simulation trajectories. We applied FRESEAN mode analysis to simulations of alanine dipeptide, a common test system for enhanced sampling simulation protocols, and compare the performance of isolated low-frequency vibrations to conventional user-defined collective variables (here backbone dihedral angles) in enhanced sampling simulations. The comparison shows that enhanced sampling along anharmoni
Experimental studies are a cornerstone of Machine Learning (ML) research. A common and often implicit assumption is that the study's results will generalize beyond the study itself, e.g., to new data. That is, repeating the same study under different conditions will likely yield similar results. Existing frameworks to measure generalizability, borrowed from the casual inference literature, cannot capture the complexity of the results and the goals of an ML study. The problem of measuring generalizability in the more general ML setting is thus still open, also due to the lack of a mathematical formalization of experimental studies. In this paper, we propose such a formalization, use it to develop a framework to quantify generalizability, and propose an instantiation based on rankings and the Maximum Mean Discrepancy. We show how our framework offers insights into the number of experiments necessary for a generalizable study, and how experimenters can benefit from it. Finally, we release the genexpy Python package, which allows for an effortless evaluation of the generalizability of other experimental studies.
Zero-Shot Learning (ZSL) presents the challenge of identifying categories not seen during training. This task is crucial in domains where it is costly, prohibited, or simply not feasible to collect training data. ZSL depends on a mapping between the visual space and available semantic information. Prior works learn a mapping between spaces that can be exploited during inference. We contend, however, that the disparity between meticulously curated semantic spaces and the inherently noisy nature of real-world data remains a substantial and unresolved challenge. In this paper, we address this by introducing a Semantic Encoder-Enhanced Representations for Zero-Shot Learning (SEER-ZSL). We propose a hybrid strategy to address the generalization gap. First, we aim to distill meaningful semantic information using a probabilistic encoder, enhancing the semantic consistency and robustness. Second, we distill the visual space by exploiting the learned data distribution through an adversarially trained generator. Finally, we align the distilled information, enabling a mapping of unseen categories onto the true data manifold. We demonstrate empirically that this approach yields a model that ou
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein's inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes' subspace, together with a severity measure that correlates with the ground truth.