Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Seeing the Earth's Interior Through Unbiased Tomographic Lenses

Geophysical tomographic studies traditionally exploit linear, damped least squares inversion methods. We demonstrate that the resulting models can be locally biased toward lower or higher amplitudes in regions of poor data illumination, potentially causing physical misinterpretations. For example, we show that global model S40RTS is locally biased toward higher amplitudes below isolated receivers where raypaths are quasi‐vertical, such as on Hawaii. This leads to questions on the apparent low‐velocity structure interpreted as the Hawaii hot spot. We prove that a linear Backus‐Gilbert inversion scheme can bring the Earth's interior into focus through unbiased tomographic lenses, as its model estimates are constrained to be averages over the true model. It also efficiently computes the full generalized inverse required to infer both model resolution and its covariance, enabling quantitative interpretations of tomographic models.
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving

Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are not available. Implicitly infusing the spatial geometric prior knowledge acquired by a duplex-encoder teacher model into a single-encoder student model is a practical, albeit less explored research avenue. This paper delves into this topic and resorts to knowledge distillation approaches to address this problem. We introduce the Learning to Infuse"X"(LIX) framework, with novel contributions in both logit distillation and feature distillation aspects. We present a mathematical proof that underscores the limitation of using a single fixed weight in decoupled knowledge distillation and introduce a logit-wise dynamic weight controller as a solution to this issue. Furthermore, we develop an adaptively-recalibrated feature distillation algorithm, including two technical novelties: feature recalibration via kernel regression and in-depth feature consistency quantification via centered kernel alignment. Extensive experiments conducted with intermediate-fusion and late-fusion networks across various public datasets provide both quantitative and qualitative evaluations, demonstrating the superior performance of our LIX framework when compared to other state-of-the-art approaches.
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods

Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with desired properties. Despite its effectiveness, the process is often challenging because of the time-consuming trial and error procedures for creating and testing different candidate augmentations and their hyperparameters manually. Automated data augmentation methods aim to automate the process. State-of-the-art approaches typically rely on automated machine learning (AutoML) principles. This work presents a comprehensive survey of AutoML-based data augmentation techniques. We discuss various approaches for accomplishing data augmentation with AutoML, including data manipulation, data integration and data synthesis techniques. We present extensive discussion of techniques for realizing each of the major subtasks of the data augmentation process: search space design, hyperparameter optimization and model evaluation. Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches. The results show that AutoML methods for data augmentation currently outperform state-of-the-art techniques based on conventional approaches.
On the development of an RFSoC-based ultra-fast Phasemeter with GHz bandwidth

Precise measurements of the frequency and phase of an electrical or optical signal play a key role in various branches of science and engineering. Tracking changing laser frequencies is specifically demanding when the lasers themselves are noisy or if the frequencies rapidly change because they encode highly dynamic signals in, e.g., Doppler-ranging or dynamic cavity readout. Here, we report the development of a high signal bandwidth (>2 GHz) and high tracking bandwidth (2 MHz) multi-channel Phasemeter. The implementation utilizes an all-digital phase-locked loop realized within the field programmable gate array (FPGA) part of a Radio Frequency System-on-Chip (RFSoC), the programmable logic. We present performance measurements, discuss the role of the high tracking bandwidth for tracking highly dynamic signals, and demonstrate ultra-stable phase locking of a beat note between two widely tunable external cavity diode lasers. We achieve a phase-noise floor in the sub-milli radian regime, even for GHz signals, and demonstrate stable tracking of signals with a frequency change rate of 240 GHz/s.
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models

The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research. Despite widespread techniques used to diminish the over-fitting issue such as data augmentation, pseudo-labeling, regularization, and ensemble learning, this performance still needs to be enhanced with other approaches. In recent years, it has been theoretically demonstrated that the loss function characteristics i.e. its Lipschitzness and maximum value affect the generalization performance of deep neural networks which can be utilized as a guidance to propose novel distance measures. In this paper, by analyzing the aforementioned characteristics, we introduce a distance called Reduced Jeffries-Matusita as a loss function for training deep classification models to reduce the over-fitting issue. In our experiments, we evaluate the new loss function in two different problems: image classification in computer vision and node classification in the context of graph learning. The results show that the new distance measure stabilizes the training process significantly, enhances the generalization ability, and improves the performance of the models in the Accuracy and F1-score metrics, even if the training set size is small.
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correction as two standalone tasks, without considering their potential relationship, especially on low-quality images where severe noise and motion artifacts occur simultaneously. To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model. In the adaptive denoising model, we first design a novel noise level estimation strategy, and then adaptively reduce the noise through a U-Net backbone with feature normalization conditioning on the estimated noise variance. The anti-artifact model employs another U-Net for eliminating motion artifacts, incorporating a novel gradient-based loss function designed to maintain the integrity of brain anatomy during the motion correction process. These two models are iteratively employed for joint image denoising and artifact correction through an iterative learning framework. An early stopping strategy depending on noise level estimation is applied to accelerate the iteration process. The denoising model is trained with 9,544 T1-weighted MRIs with manually added Gaussian noise as supervision. The anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts and paired motion-free images. Experimental results on a public dataset and a clinical study suggest the effectiveness of JDAC in both tasks of denoising and motion artifact correction, compared with several state-of-the-art methods.
Refractive COLMAP: Refractive Structure-from-Motion Revisited

In this paper, we present a complete refractive Structure-from-Motion (RSfM) framework for underwater 3D reconstruction using refractive camera setups (for both, flat- and dome-port underwater housings). Despite notable achievements in refractive multi-view geometry over the past decade, a robust, complete and publicly available solution for such tasks is not available at present, and often practical applications have to resort to approximating refraction effects by the intrinsic (distortion) parameters of a pinhole camera model. To fill this gap, we have integrated refraction considerations throughout the entire SfM process within the state-of-the-art, open-source SfM framework COLMAP. Numerical simulations and reconstruction results on synthetically generated but photo-realistic images with ground truth validate that enabling refraction does not compromise accuracy or robustness as compared to in-air reconstructions. Finally, we demonstrate the capability of our approach for large-scale refractive scenarios using a dataset consisting of nearly 6000 images. The implementation is released as open-source at: https://cau-git.rz. uni-kiel.de/inf-ag-koeser/colmap_underwater.
LiqD: A Dynamic Liquid Level Detection Model under Tricky Small Containers

In daily life and industrial production, it is crucial to accurately detect changes in liquid level in containers. Traditional contact measurement methods have some limitations, while emerging non-contact image processing technology shows good application prospects. This paper proposes a container dynamic liquid level detection model based on U^2-Net. This model uses the SAM model to generate an initial data set, and then evaluates and filters out high-quality pseudo-label images through the SemiReward framework to build an exclusive data set. The model uses U^2-Net to extract mask images of containers from the data set, and uses morphological processing to compensate for mask defects. Subsequently, the model calculates the grayscale difference between adjacent video frame images at the same position, segments the liquid level change area by setting a difference threshold, and finally uses a lightweight neural network to classify the liquid level state. This approach not only mitigates the impact of intricate surroundings, but also reduces the demand for training data, showing strong robustness and versatility. A large number of experimental results show that the proposed model can effectively detect the dynamic liquid level changes of the liquid in the container, providing a novel and efficient solution for related fields.
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach

Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are proposed to approximate graph filters by leveraging various polynomial bases for filter training. However, no existing studies have explored the diverse polynomial graph filters from a unified perspective for optimization. In this paper, we first unify polynomial graph filters, as well as the optimal filters of identical degrees into the Krylov subspace of the same order, thus providing equivalent expressive power theoretically. Next, we investigate the asymptotic convergence property of polynomials from the unified Krylov subspace perspective, revealing their limited adaptability in graphs with varying heterophily degrees. Inspired by those facts, we design a novel adaptive Krylov subspace approach to optimize polynomial bases with provable controllability over the graph spectrum so as to adapt various heterophily graphs. Subsequently, we propose AdaptKry, an optimized polynomial graph filter utilizing bases from the adaptive Krylov subspaces. Meanwhile, in light of the diverse spectral properties of complex graphs, we extend AdaptKry by leveraging multiple adaptive Krylov bases without incurring extra training costs. As a consequence, extended AdaptKry is able to capture the intricate characteristics of graphs and provide insights into their inherent complexity. We conduct extensive experiments across a series of real-world datasets. The experimental results demonstrate the superior filtering capability of AdaptKry, as well as the optimized efficacy of the adaptive Krylov basis.
Reconstructing Depth‐Averaged Open‐Channel Flows Using Image Velocimetry and Photogrammetry

Steep streams with massive sediment supply are among the most complex systems to study, even in the laboratory. Their shallow sediment‐laden flows create self‐adjusting bed geometries that evolve rapidly. Often, morphological changes and flow processes cannot be dissociated. Because these very shallow and unstable flows cannot be equipped with measurement sensors, image analysis techniques, such as photogrammetry (e.g., structure‐from‐motion, SfM) and large‐scale particle image velocimetry (LSPIV), are interesting options for capturing the characteristics of these systems. The present work describes a complete procedure using both techniques to measure spatially distributed surface velocity and bed properties (deposit patterns, channel slope, and local roughness). The velocity data are used to assess the local flow directions along which the channel slope and roughness are extracted from the SfM digital elevation models. Fergusons “variable power equation” friction law, having been previously validated by comparison with approximately 100 local flow depth measurements, was used in a second step with the collected data to reconstruct a complete mapping of the depth‐averaged flows, thereby enabling a comprehensive analysis of the hydrogeomorphic system where shallow water equations apply. The assumptions, details, use of the friction law with roughness standard deviation rather than diameter as parameter and limitations of the procedure as well as possible sources of errors are discussed here, along with possibilities for improvements. This affordable and simple‐to‐implement procedure can provide a large amount of data, allowing for a more comprehensive analysis of complex hydraulic systems.
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning

Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However, most existing FL methods assume that domain labels are provided during training, and their evaluation imposes explicit constraints on the number of domains, which must strictly match the number of clients. Because of the underutilization of numerous edge devices and additional cross-client domain annotations in the real world, such restrictions may be impractical and involve potential privacy leaks. In this paper, we propose an efficient and novel approach, called Disentangled Prompt Tuning (DiPrompT), a method that tackles the above restrictions by learning adaptive prompts for domain generalization in a distributed manner. Specifically, we first design two types of prompts, i.e., global prompt to capture general knowledge across all clients and domain prompts to capture domain-specific knowledge. They eliminate the restriction on the one-to-one mapping between source domains and local clients. Furthermore, a dynamic query metric is introduced to automatically search the suitable domain label for each sample, which includes two-substep text-image alignments based on prompt tuning without labor-intensive annotation. Extensive experiments on multiple datasets demonstrate that our DiPrompT achieves superior domain generalization performance over state-of-the-art FL methods when domain labels are not provided, and even outperforms many centralized learning methods using domain labels.
Seismic Imaging of Shallow Carbonate and Shale Hosted Massive Sulphide Deposits: A Feasibility Study

Seismic imaging of shallow (< 600 m) sediment hosted massive sulphides has not been studied in detail. In this research two shallow sediment hosted massive sulphide deposits (one deeper and larger than the other) were modeled and synthetic seismic data generated using a 2D elastic wavefield finite difference code. F-k filtering can be used to attenuating surface waves (conventional processing). This requires small trace spacing so that spatially aliasing of energy is avoided. An alternative to this involves avoiding the surface wave using an optimum offset window technique. Both of these approaches, attenuation and avoidance, produce a high amplitude image of the deeper larger orebody. For the smaller, shallower orebody, conventional processing produced only a weak image of the orebody. However, there is a significant amount of shear wave energy from this target, and therefore multicomponent geophones should be used to capture this energy.
Negative Impact of Online Political Incivility on Willingness to See Political Comments

Recently, there has been significant attention on online political incivility. While previous research suggests that uncivil political comments lead people to be less willing to see more comments on the same issue, two critical questions have received limited exploration: (1) Are people exposed to uncivil political comments less willing to see other comments from the person who posted the uncivil comment? ; (2) Are people exposed to uncivil political comments less willing to see comments from people who have different thoughts than them? To address these questions, the present study conducted a preregistered online survey experiment targeting Japanese citizens, focusing on the pro- vs anti-Kishida cabinet conflict in Japan. The results show that the participants were less willing to see other comments by the person who posted the comment when the comment was uncivil than when it was civil. In addition, the anti-Kishida participants were less willing to see political opinions posted online by people who have different thoughts than them when the comment was uncivil than when it was civil, while the participants in the other subgroups did not show a similar tendency. These findings suggest that uncivil expressions in online political communication might prompt people to avoid reading opinions from those who have different thoughts than them, which might promote political echo chambers.
Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation

Due to the dynamic and unpredictable open-world setting, navigating complex environments in Minecraft poses significant challenges for multi-agent systems. Agents must interact with the environment and coordinate their actions with other agents to achieve common objectives. However, traditional approaches often struggle to efficiently manage inter-agent communication and task distribution, crucial for effective multi-agent navigation. Furthermore, processing and integrating multi-modal information (such as visual, textual, and auditory data) is essential for agents to comprehend their goals and navigate the environment successfully and fully. To address this issue, we design the HAS framework to auto-organize groups of LLM-based agents to complete navigation tasks. In our approach, we devise a hierarchical auto-organizing navigation system, which is characterized by 1) a hierarchical system for multi-agent organization, ensuring centralized planning and decentralized execution; 2) an auto-organizing and intra-communication mechanism, enabling dynamic group adjustment under subtasks; 3) a multi-modal information platform, facilitating multi-modal perception to perform the three navigation tasks with one system. To assess organizational behavior, we design a series of navigation tasks in the Minecraft environment, which includes searching and exploring. We aim to develop embodied organizations that push the boundaries of embodied AI, moving it towards a more human-like organizational structure.
A posteriori error estimates for the Generalized Burgers-Huxley equation with weakly singular kernels

This paper explores the residual based a posteriori error estimations for the generalized Burgers-Huxley equation (GBHE) featuring weakly singular kernels. Initially, we present a reliable and efficient error estimator for both the stationary GBHE and the semi-discrete GBHE with memory, utilizing the discontinuous Galerkin finite element method (DGFEM) in spatial dimensions. Additionally, employing backward Euler and Crank Nicolson discretization in the temporal domain and DGFEM in spatial dimensions, we introduce an estimator for the fully discrete GBHE, taking into account the influence of past history. The paper also establishes optimal $L^2$ error estimates for both the stationary GBHE and GBHE. Ultimately, we validate the effectiveness of the proposed error estimator through numerical results, demonstrating its efficacy in an adaptive refinement strategy.
Understanding Reader Takeaways in Thematic Maps Under Varying Text, Detail, and Spatial Autocorrelation

Maps are crucial in conveying geospatial data in diverse contexts such as news and scientific reports. This research, utilizing thematic maps, probes deeper into the underexplored intersection of text framing and map types in influencing map interpretation. In this work, we conducted experiments to evaluate how textual detail and semantic content variations affect the quality of insights derived from map examination. We also explored the influence of explanatory annotations across different map types (e.g., choropleth, hexbin, isarithmic), base map details, and changing levels of spatial autocorrelation in the data. From two online experiments with $N=103$ participants, we found that annotations, their specific attributes, and map type used to present the data significantly shape the quality of takeaways. Notably, we found that the effectiveness of annotations hinges on their contextual integration. These findings offer valuable guidance to the visualization community for crafting impactful thematic geospatial representations.
Mixed moments of $\rm GL(2)$ and symmetric square $L$-functions

In this paper, we prove asymptotic formulas of mixed moments of $\rm GL(2)$ and its symmetric square $L$-functions for both Hecke--Maass cusp forms and holomorphic Hecke eigenforms in short intervals. As an application, we prove quantitative simultaneous non-vanishing of central values of these $L$-functions.
Help Supporters: Exploring the Design Space of Assistive Technologies to Support Face-to-Face Help Between Blind and Sighted Strangers

Blind and low-vision (BLV) people face many challenges when venturing into public environments, often wishing it were easier to get help from people nearby. Ironically, while many sighted individuals are willing to help, such interactions are infrequent. Asking for help is socially awkward for BLV people, and sighted people lack experience in helping BLV people. Through a mixed-ability research-through-design process, we explore four diverse approaches toward how assistive technology can serve as help supporters that collaborate with both BLV and sighted parties throughout the help process. These approaches span two phases: the connection phase (finding someone to help) and the collaboration phase (facilitating help after finding someone). Our findings from a 20-participant mixed-ability study reveal how help supporters can best facilitate connection, which types of information they should present during both phases, and more. We discuss design implications for future approaches to support face-to-face help.