Miracle-dz/UniFAR-FTUs huggingface dataset:
HOMOTOPY THEORY OF S-BIMODULES, NAIVE RING SPECTRA AND STABLE MODEL CATEGORIES

Acknowledgments First of all I would like to thank my supervisor Stefan Schwede for suggesting this project to me and for the support while carrying it out. I also would like to thank my colleagues from the Graduiertenkolleg " GRK 1150 Homotopy and Cohomology " for many interesting discussions, the Deutsche Forschungsgemeinschaft and staff from the GRK for their financial and other support. I want to thank Moritz Groth for making sharing an office with him a great pleasure and good place to work. Finally I want to especially thank Jan Möllers for the long and intense collaboration for almost four years, for sharing his many ideas and opinions with me and for the many, not only mathematical, discussions.
Inference from gated first-passage times

First-passage times provide invaluable insight into fundamental properties of stochastic processes. Yet, various forms of gating mask first-passage times and differentiate them from actual detection times. For instance, imperfect conditions may intermittently gate our ability to observe a system of interest, such that exact first-passage instances might be missed. In other cases, e.g., certain chemical reactions, direct observation of the molecules involved is virtually impossible, but the reaction event itself can be detected. However, this instance need not coincide with the first collision time since some molecular encounters are infertile and hence gated. Motivated by the challenge posed by such real-life situations we develop a universal -- model-free -- framework for the inference of first-passage times from the detection times of gated first-passage processes. In addition, when the underlying laws of motions are known, our framework also provides a way to infer physically meaningful parameters, e.g. diffusion coefficients. Finally, we show how to infer the gating rates themselves via the hitherto overlooked short-time regime of the measured detection times. The robustness of our approach and its insensitivity to underlying details are illustrated in several settings of physical relevance.
Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions

In an era where scientific experimentation is often costly, multi-fidelity emulation provides a powerful tool for predictive scientific computing. While there has been notable work on multi-fidelity modeling, existing models do not incorporate an important"conglomerate"property of multi-fidelity simulators, where the accuracies of different simulator components are controlled by different fidelity parameters. Such conglomerate simulators are widely encountered in complex nuclear physics and astrophysics applications. We thus propose a new CONglomerate multi-FIdelity Gaussian process (CONFIG) model, which embeds this conglomerate structure within a novel non-stationary covariance function. We show that the proposed CONFIG model can capture prior knowledge on the numerical convergence of conglomerate simulators, which allows for cost-efficient emulation of multi-fidelity systems. We demonstrate the improved predictive performance of CONFIG over state-of-the-art models in a suite of numerical experiments and two applications, the first for emulation of cantilever beam deflection and the second for emulating the evolution of the quark-gluon plasma, which was theorized to have filled the Universe shortly after the Big Bang.
Effective control of two-dimensional Rayleigh-Bénard convection: invariant multi-agent reinforcement learning is all you need

Rayleigh–Bénard convection (RBC) is a recurrent phenomenon in a number of industrial and geoscience flows and a well-studied system from a fundamental fluid-mechanics viewpoint. In the present work, we conduct numerical simulations to apply deep reinforcement learning (DRL) for controlling two-dimensional RBC using sensor-based feedback control. We show that effective RBC control can be obtained by leveraging invariant multi-agent reinforcement learning (MARL), which takes advantage of the locality and translational invariance inherent to RBC flows inside wide channels. MARL applied to RBC allows for an increase in the number of control segments without encountering the curse of dimensionality that would result from a naive increase in the DRL action-size dimension. This is made possible by the MARL ability for re-using the knowledge generated in different parts of the RBC domain. MARL is able to discover an advanced control strategy that destabilizes the spontaneous RBC double-cell pattern, changes the topology of RBC by coalescing adjacent convection cells, and actively controls the resulting coalesced cell to bring it to a new stable configuration. This modified flow configuration results in reduced convective heat transfer, which is beneficial in a number of industrial processes. We additionally draw comparisons with a conventional single-agent reinforcement learning (SARL) setup and report that in the same number of episodes, SARL is not able to learn an effective policy to control the cells. Thus, our work both shows the potential of MARL for controlling large RBC systems and demonstrates the possibility for DRL to discover strategies that move the RBC configuration between different topological configurations, yielding desirable heat-transfer characteristics.
Explaining Multimodal Data Fusion: Occlusion Analysis for Wilderness Mapping

In order to gain a better understanding of disturbances (i.e., anthropogenic pressure) in our environment, researchers have worked on methods for the mapping of wilderness areas given their crucial role in providing native habitat for many species, which are often endangered. In this work, we formulate the wilderness mapping task as a supervised learning problem. We focus on the joint use of potentially complementary features provided by multi-modal input data. Until now, the individual influences of different input modalities on the decision of a deep neural network have largely remained unclear. Therefore, we develop a framework for the modality-level interpretation of multi-modal Earth observation data in an end-to-end fashion. While leveraging an explainable machine learning method, namely Occlusion Sensitivity Maps, the proposed framework investigates the influence of modalities in an early-fusion setting, i.e. the modalities are fused before the learning process. With respect to the application, our results indicate that auxiliary data such as land cover and nighttime light data are important sources for the accurate classification of wilderness areas and the influence of a modality increases with the increasing number of spectral channels.
Novel method for the identification of the production flavor of neutral charmed mesons

We propose a new algorithm for the identification of the production flavor of neutral $D$ mesons in the Belle II experiment. The algorithm exploits the correlation between the flavor of a reconstructed neutral $D$ meson (signal $D$ meson) and the electric charges of particles reconstructed in the rest of the $e^+e^-\to c \bar{c}$ event. These include those originating from the decay of the other charm hadron produced in the event, as well as those possibly produced in association with the signal $D$ meson. We develop the algorithm using simulation and calibrate it in data using decay modes that identify the flavor of the decaying neutral $D$ meson. We use a data sample of $e^+e^-$ collisions, corresponding to $362\,\mathrm{fb}^{-1}$ of integrated luminosity, collected by Belle II at center-of-mass energies near the $\Upsilon(4S)$ mass. The effective tagging efficiency in data is $(47.91 \pm 0.07 \mathrm{(stat)} \pm 0.51 \mathrm{(syst)})\%$, independent of the neutral-$D$-meson decay mode. This charm flavor tagger will approximately double the effective sample size of many $CP$-violation and charm-mixing measurements that so far have exclusively relied on neutral $D$ mesons originating from $D^{*\pm}$ decays. While developed for Belle II, the basic principles underlying the charm flavor tagger can be used in other experiments, including those at hadron colliders.
Online augmentation of learned grasp sequence policies for more adaptable and data-efficient in-hand manipulation

When using a tool, the grasps used for picking it up, reposing, and holding it in a suitable pose for the desired task could be distinct. Therefore, a key challenge for autonomous in-hand tool manipulation is finding a sequence of grasps that facilitates every step of the tool use process while continuously maintaining force closure and stability. Due to the complexity of modeling the contact dynamics, reinforcement learning (RL) techniques can provide a solution in this continuous space subject to highly parameterized physical models. However, these techniques impose a trade-off in adaptability and data efficiency. At test time the tool properties, desired trajectory, and desired application forces could differ substantially from training scenarios. Adapting to this necessitates more data or computationally expensive online policy updates. In this work, we apply the principles of discrete dynamic programming (DP) to augment RL performance with domain knowledge. Specifically, we first design a computationally simple approximation of our environment. We then demonstrate in physical simulation that performing tree searches (i.e., lookaheads) and policy rollouts with this approximation can improve an RL-derived grasp sequence policy with minimal additional online computation. Additionally, we show that pretraining a deep RL network with the DP-derived solution to the discretized problem can speed up policy training.
Born to Be Wide: The Distribution of Wide Binaries in the Field and Soft Binaries in Clusters

Most stars, binaries, and higher-multiplicity systems are thought to form in stellar clusters and associations that later dissociate. Very wide binaries can be easily disrupted in clusters due to dynamical evaporation (soft binaries) and/or tidal disruption by the gravitational potential of the cluster. Nevertheless, wide binaries are quite frequent in the field, where they can sometimes play a key role in the formation of compact binaries and serve as tools to study key physical processes. Here we use analytic tools to study the dynamical formation of soft binaries in clusters and their survival as field binaries following cluster dispersion. We derive the expected properties of very wide binaries both in clusters and in the field. We analytically derive their detailed distributions, including the wide binary fraction as a function of mass in different cluster environments, binary mass functions and mass ratios, and the distribution of their orbital properties. We show that our calculations agree well in most aspects with the results of N-body simulations but show some different binary fraction dependence on the cluster mass. We find that the overall fraction of wide binaries scales as ∝N⋆−1 , where N ⋆ is the size of the cluster, even for non-equal-mass stars. More massive stars are more likely to capture wide companions, with most stars above 5 M ⊙ likely to capture at least one stellar companion, and formation of triples is found to be frequent.
Quantum mismatch: A powerful measure of quantumness in neutrino oscillations

The quantum nature of neutrino oscillations would be reflected in the mismatch between the neutrino survival probabilities with and without an intermediate observation. We propose this ``quantum mismatch'' as a measure of quantumness in neutrino oscillations, which precisely extracts the interference term in the two-flavor limit. In the full three-flavor scenario, we provide modified definitions of the Leggett-Garg and quantum mismatch measures. These are applicable for long-baseline and reactor neutrino experiments that measure neutrino survival probabilities with negligible matter effects.
Semantic Validation in Structure from Motion

The Structure from Motion (SfM) challenge in computer vision is the process of recovering the 3D structure of a scene from a series of projective measurements that are calculated from a collection of 2D images, taken from different perspectives. SfM consists of three main steps; feature detection and matching, camera motion estimation, and recovery of 3D structure from estimated intrinsic and extrinsic parameters and features. A problem encountered in SfM is that scenes lacking texture or with repetitive features can cause erroneous feature matching between frames. Semantic segmentation offers a route to validate and correct SfM models by labelling pixels in the input images with the use of a deep convolutional neural network. The semantic and geometric properties associated with classes in the scene can be taken advantage of to apply prior constraints to each class of object. The SfM pipeline COLMAP and semantic segmentation pipeline DeepLab were used. This, along with planar reconstruction of the dense model, were used to determine erroneous points that may be occluded from the calculated camera position, given the semantic label, and thus prior constraint of the reconstructed plane. Herein, semantic segmentation is integrated into SfM to apply priors on the 3D point cloud, given the object detection in the 2D input images. Additionally, the semantic labels of matched keypoints are compared and inconsistent semantically labelled points discarded. Furthermore, semantic labels on input images are used for the removal of objects associated with motion in the output SfM models. The proposed approach is evaluated on a data-set of 1102 images of a repetitive architecture scene. This project offers a novel method for improved validation of 3D SfM models.
Quantum algorithm for robust optimization via stochastic-gradient online learning

Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous settings has catered to a rich source of research problems. Robust convex optimization is a branch of optimization theory in which the variables or parameters involved have a certain level of uncertainty. In this work, we consider the online robust optimization meta-algorithm by Ben-Tal et al. and show that for a large range of stochastic subgradients, this algorithm has the same guarantee as the original non-stochastic version. We develop a quantum version of this algorithm and show that an at most quadratic improvement in terms of the dimension can be achieved. The speedup is due to the use of quantum state preparation, quantum norm estimation, and quantum multi-sampling. We apply our quantum meta-algorithm to examples such as robust linear programs and robust semidefinite programs and give applications of these robust optimization problems in finance and engineering.
Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Losses

This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions. We demonstrate that the optimal mapping between score values and the target region is a rectangular function. To produce this optimal rectangular fit for the observed samples, we propose a sequential approach that can its estimation with each incoming new sample. Our approach has logarithmic time complexity per iteration and is optimally efficient.
Re-Evaluating LiDAR Scene Flow for Autonomous Driving

Popular benchmarks for self-supervised LiDAR scene flow (stereoKITTI, and FlyingThings3D) have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sampling patterns. As a result, progress on these benchmarks is misleading and may cause researchers to focus on the wrong problems. We evaluate a suite of top methods on a suite of real-world datasets (Argoverse 2.0, Waymo, and NuScenes) and report several conclusions. First, we find that performance on stereoKITTI is negatively correlated with performance on real-world data. Second, we find that one of this task's key components -- removing the dominant ego-motion -- is better solved by classic ICP than any tested method. Finally, we show that despite the emphasis placed on learning, most performance gains are caused by pre- and post-processing steps: piecewise-rigid refinement and ground removal. We demonstrate this through a baseline method that combines these processing steps with a learning-free test-time flow optimization. This baseline outperforms every evaluated method.
The Impact of Ownership and Contribution Alignment on Code Technical Debt Accumulation

Software development organisations strive to maintain their effectiveness while the complexity of the systems they develop continues to grow. To tackle this challenge, organisations tend to be organised into small teams working with components that can be developed separately. Here, organisations must design their architecture and organisational structures in a way that enables communication and minimises dependencies, and helps teams reduce code and architectural degradation. Ensuring that each small independent team is responsible for components they primarily contribute is one approach to achieving this goal. This study aims at understanding the impact of ownership and contribution alignment on accumulation of code technical debt and how abrupt changes in team constellation affect teams' effectiveness in managing TD. We conducted an embedded case study in a company developing a very large software system, analysing ten components belonging to 1 team. During the studied period, the team was split into two, and the components owned by them were distributed between the two new teams. With high degrees of contribution alignment, we noticed a negative correlation between contribution alignment and TD Density before the team split. In 4 components, this correlation is statistically significant. This means a higher contribution alignment degree implies a lower TD Density. After the split, we observe a negative correlation in 3 components. The positive correlation observed in the other 5 components could be attributed to low contribution alignment, leading to difficulties in managing TD. Our findings suggest that contribution alignment can be important in controlling TD in software development organisations. Making teams responsible for the quality of components they have more expertise over and minimising dependencies between teams can help organisations mitigate the growth of TD.
Gemini Near Infrared Spectrograph–Distant Quasar Survey: Prescriptions for Calibrating UV-based Estimates of Supermassive Black Hole Masses in High-redshift Quasars

The most reliable single-epoch supermassive black hole mass (M BH) estimates in quasars are obtained by using the velocity widths of low-ionization emission lines, typically the Hβ λ4861 line. Unfortunately, this line is redshifted out of the optical band at z ≈ 1, leaving M BH estimates to rely on proxy rest-frame ultraviolet (UV) emission lines, such as C iv λ1549 or Mg ii λ2800, which contain intrinsic challenges when measuring, resulting in uncertain M BH estimates. In this work, we aim at correcting M BH estimates derived from the C iv and Mg ii emission lines based on estimates derived from the Hβ emission line. We find that employing the equivalent width of C iv in deriving M BH estimates based on Mg ii and C iv provides values that are closest to those obtained from Hβ. We also provide prescriptions to estimate M BH values when only C iv, only Mg ii, and both C iv and Mg ii are measurable. We find that utilizing both emission lines, where available, reduces the scatter of UV-based M BH estimates by ∼15% when compared to previous studies. Lastly, we discuss the potential of our prescriptions to provide more accurate and precise estimates of M BH given a much larger sample of quasars at 3.20 ≲ z ≲ 3.50, where both Mg ii and Hβ can be measured in the same near-infrared spectrum.
DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation

Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue. Existing works have proposed a variety of solutions such as boundary-aware networks, multi-stage refinement, and temporal smoothness losses. However, most of them take advantage of frame-wise supervision, which cannot effectively tackle the evaluation metrics with different granularities. In this paper, for the desirable large receptive field, we first develop a novel local-global attention mechanism with temporal pyramid dilation and temporal pyramid pooling for efficient multi-scale attention. Then we decouple two inherent goals in action segmentation, ie, (1) individual identification solved by frame-wise supervision, and (2) temporal reasoning tackled by action set prediction. Afterward, an action alignment module fuses these different granularity predictions, leading to more accurate and smoother action segmentation. We achieve state-of-the-art accuracy, eg, 82.8% (+2.6%) on GTEA and 74.7% (+1.2%) on Breakfast, which demonstrates the effectiveness of our proposed method, accompanied by extensive ablation studies. The code will be made available later.
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization

Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features, then these features are fused with language representations to generate the summary with an encoder-decoder model. The cascaded way cannot capture the semantic alignments between images and paragraphs, which are crucial to a precise summary. In this paper, we propose ViL-Sum to jointly model paragraph-level \textbf{Vi}sion-\textbf{L}anguage Semantic Alignment and Multi-Modal \textbf{Sum}marization. The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection. The joint multi-modal encoder captures the interactions between modalities, where the reordering task guides the model to learn paragraph-level semantic alignment and the selection task guides the model to selected summary-related images in the final summary. Experimental results show that our proposed ViL-Sum significantly outperforms current state-of-the-art methods. In further analysis, we find that two well-designed tasks and joint multi-modal encoder can effectively guide the model to learn reasonable paragraphs-images and summary-images relations.
Splitting Approach for Overflow Nodes in R-tree via Incremental k-means Clustering Optimization

R-tree is suitable for maintaining dynamic data and spatial query in many areas such as reverse engineering,CAD/CAM and machine vision,while CR-tree is an excellent variant of R-tree. Aiming at the deficiency in clustering result and compute cost for overflow nodes splitting algorithm of CR-tree,an incremental k-means algorithm via principal components analysis(PCA) is proposed,which can search the new cluster center along the first principal component directions in present clusters. Combining with Silhouette indicator,the algorithm is applied to solving the problem of point set clustering,which is converted from the problem of overflow nodes Splittin. Consequently the clustering results nearly global optimal can be obtained quickly. The experimental results show that the R-tree built with the algorithm based on incremental clustering is better than CR-tree and RR*-tree in synthetic performance in aspects such as the efficiency of building R-tree,storage utilization and spatial query.