In this article, we present a novel Deep Learning model based on Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells, designed as a real-time Volcano-seismic Signal Recognition (VSR) system for Distributed Acoustic Sensing (DAS) measurements. The model was trained on an extensive database of Volcano-Tectonic (VT) events derived from the co-eruptive seismicity of the 2021 La Palma eruption, recorded by a High-fidelity submarine Distributed Acoustic Sensing array (HDAS) near the eruption site. The features used for supervised model training, based on signal energy average in frequency bands, effectively enable the model to leverage spatial contextual information and the temporal evolution of volcano-seismic signals provided by the DAS technique. The proposed model not only detects the presence of VT events but also analyzes their temporal evolution, selecting and classifying their complete waveforms with an accuracy of approximately 97% for correctly detected and classified VT events. Furthermore, the model has demonstrated robust performance in generalizing to other time intervals and volcanoes, enabling continuous real-time monitoring of seismicity. Such results highlight the potential of using RNN-based approaches with LSTM cells for application to other active volcanoes, enabling fast, automatic analysis with low computational requirements and the need of minimal retraining, for the creation of labeled seismic catalogs directly from DAS measurements. This represents a significant advancement in the use of DAS technology as a viable tool to study active volcanoes and their seismic activity.

The California Community Earth Models for Seismic Hazard Assessments Workshop (https://www.scec.org/workshops/2024/california-community-models, accessed December 16, 2024) was held online on March 4-5, 2024, with more than 200 participants over two days. In this report, we provide a summary of the key points from the presentations and discussions. We highlight three use cases that drive the development of community Earth models, present an inventory of existing community Earth models in California, summarize a few techniques for integrating and merging models, discuss potential connections with the Cascadia Region Earthquake Science Center (CRESCENT), and discuss what "community" means in community Earth models. Appendix B contains the workshop agenda and Appendix C contains a list of participants.

The detection of spectral bands at 3.06 um by MicrOmega, combined with the chemical identification of other NH-containing organic molecules in Ryugu samples, suggests the presence of potential NH-bearing compounds. However, the chemical forms of these NH-rich compounds, whether associated with N-rich organics, ammonium (NH4+) salts, NH4 or NH-organics-bearing phyllosilicates, or other forms, remain to be better understood. In this study, we report the characterization of two Ryugu particles (C0050 and C0052) using multi-scale infrared (mm-reflectance, micro-FTIR, and nano-AFM-IR) and NanoSIMS techniques to constrain the nature and origin of NH-bearing components in the Ryugu asteroid. Our findings show that Ryugu's C0052 particle contains rare, micrometer-sized NH-rich organic compounds with peaks at 1660 cm-1 (mainly due to C=O stretching of the amide I band) and 1550 cm-1 (mainly due to N-H bending vibration mode of the amide II band), indicative of amide-related compounds. In contrast, these compounds are absent in C0050. Notably, nitrogen isotopic analysis reveals that these amides in C0052 are depleted in 15N (d15N = -215 +/- 92 permil), confirming their indigenous origin, while carbon and hydrogen isotopic compositions are indistinguishable from terrestrial values within errors (d13C = -22 +/- 52 and dD = 194 +/- 368 permil). The amides detected in C0052 could have formed through hydrothermal alteration from carboxylic acids and amines precursors on the Ryugu's parent planetesimal. Alternatively, they could have originated from the irradiation of 15N-depleted N-bearing ice by UV light or galactic cosmic rays, either at the surface of the asteroid in the outer Solar System or on mantle of interstellar dust grains in the interstellar medium. Amides delivered to early Earth by primitive small bodies, such as asteroid Ryugu, may have played a crucial role in prebiotic chemistry.

We challenge the prevailing assumption that LLMs must rely fully on sub-word tokens for high-quality text generation. To this end, we propose the "Generative Pretrained Thoughtformer" (GPTHF), a hierarchical transformer language model capable of text generation by compressing text into sentence embeddings and employing a sentence attention mechanism. GPTHF retains GPT's architecture, modifying only token interactions via dynamic sparse attention masks. Our experiments show that GPTHF achieves an up to an order of magnitude improvement in FLOPs efficiency and a threefold increase in runtime speed compared to equally-sized GPT models in the low-size regime. This is achieved through a unique generation method that caches and reuses sentence embeddings, allowing significant portions of the input to bypass large parts of the network.

Recently, there has been an increasing interest in employing rotational motion measurements for seismic source inversion, structural imaging and ambient noise analysis. We derive reciprocity and representation theorems for rotational motion. The representations express the rotational motion inside an inhomogeneous anisotropic earth in terms of translational and rotational motion at the surface. The theorems contribute to the theoretical basis for rotational seismology methodology, such as determining the moment tensor of earthquake sources.

Deep reinforcement learning (DRL) has had success in virtual and simulated domains, but due to key differences between simulated and real-world environments, DRL-trained policies have had limited success in real-world applications. To assist researchers to bridge the \textit{sim-to-real gap}, in this paper, we describe a low-cost physical inverted pendulum apparatus and software environment for exploring sim-to-real DRL methods. In particular, the design of our apparatus enables detailed examination of the delays that arise in physical systems when sensing, communicating, learning, inferring and actuating. Moreover, we wish to improve access to educational systems, so our apparatus uses readily available materials and parts to reduce cost and logistical barriers. Our design shows how commercial, off-the-shelf electronics and electromechanical and sensor systems, combined with common metal extrusions, dowel and 3D printed couplings provide a pathway for affordable physical DRL apparatus. The physical apparatus is complemented with a simulated environment implemented using a high-fidelity physics engine and OpenAI Gym interface.

Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting pavement marking quality during daytime using the You Only Look Once (YOLO) model, leveraging its advanced architectural features to enhance road safety through precise and real-time assessments. Utilizing image data from New Jersey, this study employed three YOLOv8 variants: YOLOv8m, YOLOv8n, and YOLOv8x. The models were evaluated based on their prediction accuracy for classifying pavement markings into good, moderate, and poor visibility categories. The results demonstrated that YOLOv8n provides the best balance between accuracy and computational efficiency, achieving the highest mean Average Precision (mAP) for objects with good visibility and demonstrating robust performance across various Intersections over Union (IoU) thresholds. This research enhances transportation safety by offering an automated and accurate method for evaluating the quality of pavement markings.

Earth observation (EO) data features diverse sensing platforms with varying spectral bands, spatial resolutions, and sensing modalities. While most prior work has constrained inputs to fixed sensors, a new class of any-sensor foundation models able to process arbitrary sensors has recently emerged. Contributing to this line of work, we propose Panopticon, an any-sensor foundation model built on the DINOv2 framework. We extend DINOv2 by (1) treating images of the same geolocation across sensors as natural augmentations, (2) subsampling channels to diversify spectral input, and (3) adding a cross attention over channels as a flexible patch embedding mechanism. By encoding the wavelength and modes of optical and synthetic aperture radar sensors, respectively, Panopticon can effectively process any combination of arbitrary channels. In extensive evaluations, we achieve state-of-the-art performance on GEO-Bench, especially on the widely-used Sentinel-1 and Sentinel-2 sensors, while out-competing other any-sensor models, as well as domain adapted fixed-sensor models on unique sensor configurations. Panopticon enables immediate generalization to both existing and future satellite platforms, advancing sensor-agnostic EO.

Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance. Calibration is achieved through set predictions that are updated via online rules so as to ensure long-term coverage guarantees. While recent research has demonstrated the benefits of incorporating prior knowledge into the calibration process, this has come at the cost of replacing coverage guarantees with less tangible regret guarantees based on the quantile loss. This work introduces intermittent mirror online conformal prediction (IM-OCP), a novel runtime calibration framework that integrates prior knowledge, while maintaining long-term coverage and achieving sub-linear regret. IM-OCP features closed-form updates with minimal memory complexity, and is designed to operate under potentially intermittent feedback.

We introduce and study the spherical dimension, a natural topological relaxation of the VC dimension that unifies several results in learning theory where topology plays a key role in the proofs. The spherical dimension is defined by extending the set of realizable datasets (used to define the VC dimension) to the continuous space of realizable distributions. In this space, a shattered set of size d (in the VC sense) is completed into a continuous object, specifically a d-dimensional sphere of realizable distributions. The spherical dimension is then defined as the dimension of the largest sphere in this space. Thus, the spherical dimension is at least the VC dimension. The spherical dimension serves as a common foundation for leveraging the Borsuk-Ulam theorem and related topological tools. We demonstrate the utility of the spherical dimension in diverse applications, including disambiguations of partial concept classes, reductions from classification to stochastic convex optimization, stability and replicability, and sample compression schemes. Perhaps surprisingly, we show that the open question posed by Alon, Hanneke, Holzman, and Moran (FOCS 2021) of whether there exist non-trivial disambiguations for halfspaces with margin is equivalent to the basic open question of whether the VC and spherical dimensions are finite together.

In the fields of computer vision (CV) and remote sensing (RS), foundational models typically follow the "big data + large model parameters" paradigm. However, the application of this strategy in seismic data processing faces several challenges: seismic data is difficult to obtain and the scarcity of publicly available datasets make it difficult to construct large-scale datasets. Additionally, the high computational cost associated with a large number of model parameters restricts widespread research in this domain. Therefore, we propose a lightweight seismic processing foundational model paradigm (SPFM), which aims to overcome the limitations of traditional methods by data engineering and network architecture innovation. Specifically, we propose an innovative dataset construction strategy that generates more seismic data by data augmentation techniques, including collecting publicly available field data and using generative diffusion models (GDM) for data enhancement. Furthermore, we optimize the data distribution by employing dimensionality reduction, cluster analysis, and stratified sampling methods, reducing redundant information while preserving important seismic features, thus constructing a comprehensive dataset. In terms of network architecture design, we introduce the selective structured state-space model (Mamba) structure, which effectively captures global features of seismic data and alleviates the quadratic growth of computational complexity inherent in Transformer-based models, thereby improving computational efficiency. This model, pre-trained with only four A800 GPUs, outperforms traditional methods across multiple tasks, including denoising, interpolation, frequency-band extrapolation, and resolution enhancement. The lightweight paradigm provides an solution for seismic data processing, advancing the generalization and accessibility of seismic data processing.

Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current motion metrics do not fully align with human perceptions; 2) the existing motion prompts are limited. Based on these findings, we introduce VMBench--a comprehensive Video Motion Benchmark that has perception-aligned motion metrics and features the most diverse types of motion. VMBench has several appealing properties: 1) Perception-Driven Motion Evaluation Metrics, we identify five dimensions based on human perception in motion video assessment and develop fine-grained evaluation metrics, providing deeper insights into models' strengths and weaknesses in motion quality. 2) Meta-Guided Motion Prompt Generation, a structured method that extracts meta-information, generates diverse motion prompts with LLMs, and refines them through human-AI validation, resulting in a multi-level prompt library covering six key dynamic scene dimensions. 3) Human-Aligned Validation Mechanism, we provide human preference annotations to validate our benchmarks, with our metrics achieving an average 35.3% improvement in Spearman's correlation over baseline methods. This is the first time that the quality of motion in videos has been evaluated from the perspective of human perception alignment. Additionally, we will soon release VMBench at https://github.com/GD-AIGC/VMBench, setting a new standard for evaluating and advancing motion generation models.

Navigation and manipulation in open-world environments remain unsolved challenges in the Embodied AI. The high cost of commercial mobile manipulation robots significantly limits research in real-world scenes. To address this issue, we propose AhaRobot, a low-cost and fully open-source dual-arm mobile manipulation robot system with a hardware cost of only $1,000 (excluding optional computational resources), which is less than 1/15 of the cost of popular mobile robots. The AhaRobot system consists of three components: (1) a novel low-cost hardware architecture primarily composed of off-the-shelf components, (2) an optimized control solution to enhance operational precision integrating dual-motor backlash control and static friction compensation, and (3) a simple remote teleoperation method RoboPilot. We use handles to control the dual arms and pedals for whole-body movement. The teleoperation process is low-burden and easy to operate, much like piloting. RoboPilot is designed for remote data collection in embodied scenarios. Experimental results demonstrate that RoboPilot significantly enhances data collection efficiency in complex manipulation tasks, achieving a 30% increase compared to methods using 3D mouse and leader-follower systems. It also excels at completing extremely long-horizon tasks in one go. Furthermore, AhaRobot can be used to learn end-to-end policies and autonomously perform complex manipulation tasks, such as pen insertion and cleaning up the floor. We aim to build an affordable yet powerful platform to promote the development of embodied tasks on real devices, advancing more robust and reliable embodied AI. All hardware and software systems are available at https://aha-robot.github.io.

The integration of machine learning (ML) has significantly enhanced the capabilities of Earth Observation (EO) systems by enabling the extraction of actionable insights from complex datasets. However, the performance of data-driven EO applications is heavily influenced by the data collection and transmission processes, where limited satellite bandwidth and latency constraints can hinder the full transmission of original data to the receivers. To address this issue, adopting the concepts of Semantic Communication (SC) offers a promising solution by prioritizing the transmission of essential data semantics over raw information. Implementing SC for EO systems requires a thorough understanding of the impact of data processing and communication channel conditions on semantic loss at the processing center. This work proposes a novel data-fitting framework to empirically model the semantic loss using real-world EO datasets and domain-specific insights. The framework quantifies two primary types of semantic loss: (1) source coding loss, assessed via a data quality indicator measuring the impact of processing on raw source data, and (2) transmission loss, evaluated by comparing practical transmission performance against the Shannon limit. Semantic losses are estimated by evaluating the accuracy of EO applications using four task-oriented ML models, EfficientViT, MobileViT, ResNet50-DINO, and ResNet8-KD, on lossy image datasets under varying channel conditions and compression ratios. These results underpin a framework for efficient semantic-loss modeling in bandwidth-constrained EO scenarios, enabling more reliable and effective operations.

Many geophysical and geochemical phenomena in the Earth's interior are related to physical and chemical processes in the outer core and the core-mantle boundary, directly linked to isotopic composition. Determining the composition using standard geophysical methods has been a challenge. The oscillations of atmospheric neutrinos, influenced by their weak interactions with terrestrial matter, offer a new way to gather valuable information about the Earth's internal structure and, in particular, to constrain the core composition. If neutrinos had as yet unknown non-standard interactions (NSI), this could affect their propagation in matter and consequently impact studies of Earth's composition using neutrino oscillation tomography. This study focuses on scalar-mediated NSI and their potential impact on atmospheric neutrino oscillations, which could obscure information about the hydrogen content in the outer core. In turn, compositional uncertainties could affect the characterization of NSI parameters. The analysis is based on a Monte-Carlo simulation of the energy distribution and azimuthal angles of neutrino-generated $\mu$ events. Using a model of the Earth consisting of 55 concentric shells with constant densities determined from the PREM, we evaluate the effect on the number of events due to changes in the outer core composition (Z/A)$_{oc}$ and the NSI strength parameter $\epsilon$. To examine the detection capability to observe such variations, we consider regions in the plane of (Z/A)$_{oc}$ and $\epsilon$ where the statistical significance of the discrepancies between the modified Earth model and the reference model is less than $1\sigma$.

The Tibetan Plateau (TP) and surrounding regions, vital to global energy and water cycles, are profoundly influenced by climate change and anthropogenic activities. Despite widespread attention to vegetation greening across the region since the 1980s, its underlying mechanisms remain poorly understood. This study employs the eigen microstates method to quantify vegetation greening dynamics using long-term remote sensing and reanalysis data. We identify two dominant modes that collectively explain more than 61% of the vegetation dynamics. The strong seasonal heterogeneity in the southern TP, primarily driven by radiation and agricultural activities, is reflected in the first mode, which accounts for 46.34% of the variance. The second mode, which explains 15% of the variance, is closely linked to deep soil moisture (SM3, 28 cm to 1 m). Compared to precipitation and surface soil moisture (SM1 and SM2, 0 to 28 cm), our results show that deep soil moisture exerts a stronger and more immediate influence on vegetation growth, with a one-month response time. This study provides a complexity theory-based framework to quantify vegetation dynamics and underscores the critical influence of deep soil moisture on greening patterns in the TP.

The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which may not suit the needs of different downstream tasks. To address this limitation, we propose a model named FlexiReg for urban region representation learning that is flexible with both the formation of urban regions and the input region features. FlexiReg is based on a spatial grid partitioning over the spatial area of interest. It learns representations for the grid cells, leveraging publicly accessible data, including POI, land use, satellite imagery, and street view imagery. We propose adaptive aggregation to fuse the cell representations and prompt learning techniques to tailor the representations towards different tasks, addressing the needs of varying formations of urban regions and downstream tasks. Extensive experiments on five real-world datasets demonstrate that FlexiReg outperforms state-of-the-art models by up to 202% in term of the accuracy of four diverse downstream tasks using the produced urban region representations.

It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losses of lives and economic damage. Beyond these losses, comprehensive assessment on societal well-being, ecosystem services, and carbon dynamics are often understudied. The rapid expansion of geospatial, atmospheric, and socioeconomic data provides an unprecedented opportunity to develop systemic indices to account for a more comprehensive spectrum of Earth system change risks and to assess their socioeconomic impacts. We propose a novel approach based on the concept of syndromes that can integrate synchronized changes in biosphere, atmosphere, and socioeconomic trajectories into distinct co-evolving phenomena. While the syndrome concept was applied in policy related to environmental conservation, it has not been deciphered from systematic data-driven approaches capable of providing a more comprehensive diagnosis of anthropogenic impacts. By advocating interactive dimensionality reduction approaches, we can identify key interconnected socio-environmental changes as syndromes from big data. We recommend future research tailoring syndromes by incorporating granular data, particularly socio-economic, into dimensionality reduction at different spatio-temporal scales to better diagnose regional-to-global atmospheric and environmental changes that are relevant for socioeconomic changes.

Foundation models have had a significant impact across various AI applications, enabling use cases that were previously impossible. Contrastive Visual Language Models (VLMs), in particular, have outperformed other techniques in many tasks. However, their prevalence in remote sensing (RS) is still limited, due to the scarcity of diverse remote-sensing visual-language datasets. In this work we introduce two novel image-caption datasets for training of remote sensing foundation models. The first dataset pairs aerial and satellite imagery with captions generated by Gemini using landmarks extracted from Google Maps. The second dataset utilizes public web images and their corresponding alt-text, filtered for the remote sensing domain, resulting in a diverse dataset with greater breadth in image styles and subject matter. These datasets are used to pre-train the MaMMUT~\citep{kuo2023mammutsimplearchitecturejoint} VLM architecture, resulting in state-of-the-art generalization performance in zero-shot cross-modal retrieval on well-known public benchmarks. Finally, we present our ongoing research to distill image-level knowledge gained in the VLM contrastive training procedure to enhance the model's localization ability. Specifically, we iteratively generate pseudo-labels for image regions based on the model's attention maps and use these labels for further training. To mitigate noisy attention maps and create robust segmentation masks, we introduce a novel attention-pooling mechanism called the Smooth-Attention-Operation.

Remote sensing imagery from systems such as Sentinel provides full coverage of the Earth's surface at around 10-meter resolution. The remote sensing community has transitioned to extensive use of deep learning models due to their high performance on benchmarks such as the UCMerced and ISPRS Vaihingen datasets. Convolutional models such as UNet and ResNet variations are commonly employed for remote sensing but typically only accept three channels, as they were developed for RGB imagery, while satellite systems provide more than ten. Recently, several transformer architectures have been proposed for remote sensing, but they have not been extensively benchmarked and are typically used on small datasets such as Salinas Valley. Meanwhile, it is becoming feasible to obtain dense spatial land-use labels for entire first-level administrative divisions of some countries. Scaling law observations suggest that substantially larger multi-spectral transformer models could provide a significant leap in remote sensing performance in these settings. In this work, we propose ChromaFormer, a family of multi-spectral transformer models, which we evaluate across orders of magnitude differences in model parameters to assess their performance and scaling effectiveness on a densely labeled imagery dataset of Flanders, Belgium, covering more than 13,500 km^2 and containing 15 classes. We propose a novel multi-spectral attention strategy and demonstrate its effectiveness through ablations. Furthermore, we show that models many orders of magnitude larger than conventional architectures, such as UNet, lead to substantial accuracy improvements: a UNet++ model with 23M parameters achieves less than 65% accuracy, while a multi-spectral transformer with 655M parameters achieves over 95% accuracy on the Biological Valuation Map of Flanders.

The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow that continually updates the subsurface uncertainties and captures the latest geological understanding given the most recent observations in real-time. We propose "DISTINGUISH": a real-time, AI-driven workflow designed to transform geosteering by integrating Generative Adversarial Networks (GANs) for geological parameterization, ensemble methods for model updating, and global discrete dynamic programming (DDP) optimization for complex decision-making during directional drilling operations. The DISTINGUISH framework relies on offline training of a GAN model to reproduce relevant geology realizations and a Forward Neural Network (FNN) to model Logging-While-Drilling (LWD) tools' response for a given geomodel. This paper introduces a first-of-its-kind workflow that progressively reduces GAN-geomodel uncertainty around and ahead of the drilling bit and adjusts the well plan accordingly. The workflow automatically integrates real-time LWD data with a DDP-based decision support system, enhancing predictive models of geology ahead of drilling and leading to better steering decisions. We present a simple yet representative benchmark case and document the performance target achieved by the DISTINGUISH workflow prototype. This benchmark will be a foundation for future methodological advancements and workflow refinements.

Recent image generation schemes typically capture image distribution in a pre-constructed latent space relying on a frozen image tokenizer. Though the performance of tokenizer plays an essential role to the successful generation, its current evaluation metrics (e.g. rFID) fail to precisely assess the tokenizer and correlate its performance to the generation quality (e.g. gFID). In this paper, we comprehensively analyze the reason for the discrepancy of reconstruction and generation qualities in a discrete latent space, and, from which, we propose a novel plug-and-play tokenizer training scheme to facilitate latent space construction. Specifically, a latent perturbation approach is proposed to simulate sampling noises, i.e., the unexpected tokens sampled, from the generative process. With the latent perturbation, we further propose (1) a novel tokenizer evaluation metric, i.e., pFID, which successfully correlates the tokenizer performance to generation quality and (2) a plug-and-play tokenizer training scheme, which significantly enhances the robustness of tokenizer thus boosting the generation quality and convergence speed. Extensive benchmarking are conducted with 11 advanced discrete image tokenizers with 2 autoregressive generation models to validate our approach. The tokenizer trained with our proposed latent perturbation achieve a notable 1.60 gFID with classifier-free guidance (CFG) and 3.45 gFID without CFG with a $\sim$400M generator. Code: https://github.com/lxa9867/ImageFolder.

Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-conditioned behavior, but benchmarks are still dominated by simulation. In this work, we focus on the fine-grained behavior of fast-moving real robots and present a large-scale experimental study involving \numepisodes{} navigation episodes in a real environment with a physical robot, where we analyze the type of reasoning emerging from end-to-end training. In particular, we study the presence of realistic dynamics which the agent learned for open-loop forecasting, and their interplay with sensing. We analyze the way the agent uses latent memory to hold elements of the scene structure and information gathered during exploration. We probe the planning capabilities of the agent, and find in its memory evidence for somewhat precise plans over a limited horizon. Furthermore, we show in a post-hoc analysis that the value function learned by the agent relates to long-term planning. Put together, our experiments paint a new picture on how using tools from computer vision and sequential decision making have led to new capabilities in robotics and control. An interactive tool is available at europe.naverlabs.com/research/publications/reasoning-in-visual-navigation-of-end-to-end-trained-agents.

While parameter-efficient transfer learning (PETL) successfully reduces trainable parameters for adapting large pre-trained models, conventional methods exhibit limited effectiveness in decreasing activation memory consumption - a critical bottleneck for deployment on resource-constrained devices. We present Structure-Activation Synergy (S2A), an innovative framework achieving dual optimization of parameters and memory through two synergistic mechanisms: (1) Structural activation modules (bias/prompt/side adaptations) that strategically minimize both parametric complexity and intermediate feature storage requirements, and (2) Derivative-aware 4-bit quantization for non-parametric operators that maintains model fidelity through gradient-informed precision allocation. Extensive evaluations across multiple architectures (ViT, Swin, ResNet) and datasets (ImageNet-1K, CIFAR, DomainNet) demonstrate S2A's superior efficiency, reducing GPU memory consumption by 75\% (4.2 average reduction) while maintaining 98.7\% of full fine-tuning accuracy with only 0.9\% tunable parameters. This hardware-aware paradigm establishes new state-of-the-art in efficient model adaptation, offering practical deployment advantages through simultaneous parameter and memory optimization without compromising model capability

Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite imagery for diverse downstream tasks. Currently, GFMs primarily focus on discriminative objectives, such as contrastive learning or masked image modeling, owing to their proven success in learning transferable representations. However, generative diffusion models--which demonstrate the potential to capture multi-grained semantics essential for RS tasks during image generation--remain underexplored for discriminative applications. This prompts the question: can generative diffusion models also excel and serve as GFMs with sufficient discriminative power? In this work, we answer this question with SatDiFuser, a framework that transforms a diffusion-based generative geospatial foundation model into a powerful pretraining tool for discriminative RS. By systematically analyzing multi-stage, noise-dependent diffusion features, we develop three fusion strategies to effectively leverage these diverse representations. Extensive experiments on remote sensing benchmarks show that SatDiFuser outperforms state-of-the-art GFMs, achieving gains of up to +5.7% mIoU in semantic segmentation and +7.9% F1-score in classification, demonstrating the capacity of diffusion-based generative foundation models to rival or exceed discriminative GFMs. Code will be released.

Soil-dwelling organisms have evolved diverse strategies for efficient subterranean movement. For example, the seeds of Erodium cicutarium and Pelargonium species employ continuous rotational motion for self-burial, while the angled worm lizard Agamodon angeliceps tunnels by oscillating its head around its trunk's axis. These rotational movements significantly reduce penetration resistance. This study presents comprehensive experiments investigating the effects of various factors on rotational penetration forces and energy consumption. Results reveal that force reduction follow an approximately hyperbolic decay with the tangential-to-axial velocity ratio ($u$). Penetrator geometry, particularly roundness and conical tip shape, is found to significantly influence reduction at low velocity ratios, whereas relative density and material type exhibit moderate impact. Reduction is also observed to increase with interfacial friction angle but decreases with confining pressure and depth. Energy consumption analysis shows that while penetration force-related energy decreases with $u$, total energy consumption increases due to rotational torque. For self-burrowing robot designs, lower velocity ratios are recommended to balance penetration force reduction and energy efficiency effectively.

Oxygen concentration in tumor micro-environment is a well-established signal that can induce aggressive cancer behaviour. In particular, low oxygen levels (hypoxia) activate the Hypoxia-Inducible Factor(HIF) pathway which has an array of target systems. One of these systems is Integrin-Linked Kinase (ILK) pathway, which influences key signaling pathways for cell survival, proliferation, and migration. Hence, this paper aimed to explore the interconnection between these two pathways. Using the Petri net modeling tool Snoopy, an established HIF network model was transformed to be a continuous Petri net. Subsequently, the network was expanded to incorporate a feedback element from the ILK pathway to HIF, based on gene expression data. The resulting model conserved the oxygen switch response of the original HIF model and positively amplified HIF's output. Therefore, this model provides a starting point for establishing a system reflecting crucial effect on hypoxia-induced cancer behavior, and could potentially serve as a basis for future drug development.

The adoption of Millimeter-Wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of Open-set Gait Recognition (OSGR) from sparse mmWave radar point clouds. Unlike most existing research, which assumes a closed-set scenario, our work considers the more realistic open-set case, where unknown subjects might be present at inference time, and should be correctly recognized by the system. Point clouds are well-suited for edge computing applications with resource constraints, but are more significantly affected by noise and random fluctuations than other representations, like the more common micro-Doppler signature. This is the first work addressing open-set gait recognition with sparse point cloud data. To do so, we propose a novel neural network architecture that combines supervised classification with unsupervised reconstruction of the point clouds, creating a robust, rich, and highly regularized latent space of gait features. To detect unknown subjects at inference time, we introduce a probabilistic novelty detection algorithm that leverages the structured latent space and offers a tunable trade-off between inference speed and prediction accuracy. Along with this paper, we release mmGait10, an original human gait dataset featuring over five hours of measurements from ten subjects, under varied walking modalities. Extensive experimental results show that our solution attains F1-Score improvements by 24% over state-of-the-art methods, on average, and across multiple openness levels.

Computational modeling of faulting processes is an essential tool for understanding earthquake mechanics but remains challenging due to the structural and material complexities of fault zones. The phase-field method has recently enabled unified modeling of fault propagation and off-fault damage; however, its capability has been restricted to simplified anti-plane settings. In this study, we extend the phase-field method to in-plane faulting by introducing two key advancements: (i) the incorporation of enhanced fault kinematics and pressure-dependent shear strength for a more accurate representation of fault behavior, and (ii) a revised fault propagation criterion that explicitly accounts for the coupling between shear strength and normal stress. The proposed formulation is verified against standard discontinuous approaches to quasi-dynamic fault rupture under in-plane conditions and validated using experimental observations and numerical data on fault nucleation and propagation. Simulations incorporating structural complexities and material heterogeneities demonstrate the robustness and versatility of the phase-field model, establishing it as a powerful tool for investigating the interactions between fault zone properties and earthquake processes.

Sea surface temperature (SST) is a fundamental physical parameter characterising the thermal state of sea surface. The Thermal Infrared Sensor (TIRS) onboard Landsat-8, with its 100-meter spatial resolution, offers a unique opportunity to uncover fine-scale coastal SST patterns that would otherwise be overlooked by coarser-resolution thermal sensors. In this study, we first develop an operational approach for SST retrieval from the TIRS sensor, and subsequently propose a novel algorithm for establishing daily SST climatology which serves as the baseline to detect anomalous SST events. We applied the proposed methods to temperate coastal waters in South Australia for the ten-year period from 2014 to 2023. For ground validation purposes, a buoy was deployed off the coast of Port Lincoln, South Australia, to record in-situ time-series SST. The spatiotemporal patterns of SST in the study area were analysed based on the ten years of satellite-derived SST imagery. The daily baseline climatology of SST with 100 m resolution was constructed, which allowed for the detection and analysis of anomalous SST events during the study period of 2014-2023. Our results suggest the following: (1) the satellite-derived SST data, generated with the proposed algorithm, aligned well with the in-situ measured SST values; (2) the semi-enclosed, shallow regions of Upper Spencer Gulf and Upper St Vincent Gulf showed higher temperatures during summer and cooler temperatures during winter than waters closer to the open ocean, resulting in a higher seasonal variation in SST; (3) the near-shore shallow areas in Spencer Gulf and St Vincent Gulf, and regions surrounding Kangaroo Island, were identified to have a higher probability of SST anomalies compared to the rest of the study area; and (4) anomalous SST events were more likely to happen during the warm months than the cool months.

Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysis, and action recognition. Real-world time series datasets typically exhibit complex dynamics. To capture this complexity, RNN-based, CNN-based, Transformer-based, and hybrid models have been proposed. Unfortunately, current deep learning-based methods often neglect the simultaneous construction of local features and global dependencies at different time scales, lacking sufficient feature extraction capabilities to achieve satisfactory classification accuracy. To address these challenges, we propose a novel Multiscale Periodic Time Series Network (MPTSNet), which integrates multiscale local patterns and global correlations to fully exploit the inherent information in time series. Recognizing the multi-periodicity and complex variable correlations in time series, we use the Fourier transform to extract primary periods, enabling us to decompose data into multiscale periodic segments. Leveraging the inherent strengths of CNN and attention mechanism, we introduce the PeriodicBlock, which adaptively captures local patterns and global dependencies while offering enhanced interpretability through attention integration across different periodic scales. The experiments on UEA benchmark datasets demonstrate that the proposed MPTSNet outperforms 21 existing advanced baselines in the MTSC tasks.

In tomato greenhouse, phenotypic measurement is meaningful for researchers and farmers to monitor crop growth, thereby precisely control environmental conditions in time, leading to better quality and higher yield. Traditional phenotyping mainly relies on manual measurement, which is accurate but inefficient, more importantly, endangering the health and safety of people. Several studies have explored computer vision-based methods to replace manual phenotyping. However, the 2D-based need extra calibration, or cause destruction to fruit, or can only measure limited and meaningless traits. The 3D-based need extra depth camera, which is expensive and unacceptable for most farmers. In this paper, we propose a non-contact tomato fruit phenotyping method, titled TomatoScanner, where RGB image is all you need for input. First, pixel feature is extracted by instance segmentation of our proposed EdgeYOLO with preprocessing of individual separation and pose correction. Second, depth feature is extracted by depth estimation of Depth Pro. Third, pixel and depth feature are fused to output phenotype results in reality. We establish self-built Tomato Phenotype Dataset to test TomatoScanner, which achieves excellent phenotyping on width, height, vertical area and volume, with median relative error of 5.63%, 7.03%, -0.64% and 37.06%, respectively. We propose and add three innovative modules - EdgeAttention, EdgeLoss and EdgeBoost - into EdgeYOLO, to enhance the segmentation accuracy on edge portion. Precision and mean Edge Error greatly improve from 0.943 and 5.641% to 0.986 and 2.963%, respectively. Meanwhile, EdgeYOLO keeps lightweight and efficient, with 48.7 M weights size and 76.34 FPS. Codes and datasets: https://github.com/AlexTraveling/TomatoScanner.

Like Johnson noise, where thermal fluctuations of charge carriers in a resistor lead to measurable current fluctuations, the internal variability of Earth's atmosphere leads to fluctuations in the infrared radiation emitted to space, creating "Earth's infrared background" (EIB). This background consists of fluctuations that are isotropic in space and red in time, with an upper bound of 400 km and 2.5 days on their spatiotemporal decorrelation, between meso-scale and synoptic-scale weather. Like the anisotropies in the Cosmic Microwave Background (CMB), which represent features of interest in the Universe, the anisotropies in Earth's infrared radiation represent features of interest in Earth's atmosphere. Unlike the CMB, which represents a historical record of the Universe since the Big Bang, the EIB represents Earth's climate in steady state.

Frequent significant deviations of the observed magnitude distribution of anthropogenic seismicity from the Gutenberg-Richter relation require alternative estimation methods for probabilistic seismic hazard assessments. We evaluate five nonparametric kernel density estimation (KDE) methods on simulated samples drawn from four magnitude distribution models: the exponential, concave and convex bi-exponential, and exponential-Gaussian distributions. The latter three represent deviations from the Gutenberg-Richter relation due to the finite thickness of the seismogenic crust and the effect of characteristic earthquakes. The assumed deviations from exponentiality are never more than those met in practice. The studied KDE methods include Silverman's and Scott's rules with Abramson's bandwidth adaptation, two diffusion-based methods (ISJ and diffKDE), and adaptiveKDE, which formulates the bandwidth estimation as an optimization problem. We assess their performance for magnitudes from 2 to 6 with sample sizes of 400 to 5000, using the mean integrated square error (MISE) over 100,000 simulations. Their suitability in hazard assessments is illustrated by the mean of the mean return period (MRP) for a sample size of 1000. Among the tested methods, diffKDE provides the most accurate cumulative distribution function estimates for larger magnitudes. Even when the data is drawn from an exponential distribution, diffKDE performs comparably to maximum likelihood estimation when the sample size is at least 1000. Given that anthropogenic seismicity often deviates from the exponential model, we recommend using diffKDE for probabilistic seismic hazard assessments whenever a sufficient sample size is available.

We present the first extensive analysis of K/Ka-band ranging post-fit residuals of an official Level-2 product, characterised as Line-of-Sight Gravity Differences (LGD), which exhibit and showcase interesting sub-monthly geophysical signals. These residuals, provided by CSR, were derived from the difference between spherical harmonic coefficient least-squares fits and reduced Level-1B range-rate observations. We classified the geophysical signals into four distinct categories: oceanic, meteorological, hydrological, and solid Earth, focusing primarily on the first three categories in this study. In our examination of oceanic processes, we identified notable mass anomalies in the Argentine basin, specifically within the Zapiola Rise, where persistent remnants of the rotating dipole-like modes are evident in the LGD post-fit residuals. Our analysis extended to the Gulf of Carpentaria and Australia during the 2013 Oswald cyclone, revealing significant LGD residual anomalies that correlate with cyclone tracking and precipitation data. Additionally, we investigated the monsoon seasons in Bangladesh, particularly from June to September 2007, where we observed peaks in sub-monthly variability. These findings were further validated by demonstrating high spatial and temporal correlations between gridded LGD residuals and ITSG-Grace2018 daily solutions. Given that these anomalies are associated with significant mass change phenomena, it is essential to integrate the post-fit residuals into a high-frequency mass change framework, with the purpose of providing enhanced spatial resolution compared to conventional Kalman-filtered methods.

Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing imagery to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. The code of SpikeSR will be available upon paper acceptance.

Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or frequently observed failure scenarios, limiting the effectiveness in machine learning-based prediction. To address this challenge, this study proposes a framework for constructing balanced datasets that include distinct failure modes. The framework consists of three key steps. First, critical ground motion features (GMFs) are identified to effectively represent ground motion time histories. Second, an adaptive algorithm is employed to estimate the probability densities of various failure domains in the space of critical GMFs and structural parameters. Third, samples generated from these probability densities are transformed into ground motion time histories by using a scaling factor optimization process. A balanced dataset is constructed by performing nonlinear response history analyses on structural systems with parameters matching the generated samples, subjected to corresponding transformed ground motion time histories. Deep neural network models are trained on balanced and imbalanced datasets to highlight the importance of dataset balancing. To further evaluate the framework's applicability, numerical investigations are conducted using two different structural models subjected to recorded and synthetic ground motions. The results demonstrate the framework's robustness and effectiveness in addressing dataset imbalance and improving machine learning performance in seismic failure mode prediction.

We have explored the temporal variability of the seismicity at global scale over the last 124 years, as well as its potential drivers. To achieve this, we constructed and analyzed an averaged global seismicity curve for earthquakes of magnitude equal or greater than 6 since 1900. Using Singular Spectrum Analysis, we decomposed this curve and compared the extracted pseudo-cycles with two global geophysical parameters associated with Earth's tides: length-of-day variations and sea-level changes. Our results reveal that these three geophysical phenomena can be be explained with 90% accuracy, as the sum of up to seven periodic components, largely aligned with planetary ephemerides: 1 year, 3.4 years (Quasi-Biennial Oscillation, QBO), $\sim$11 years, $\sim$14 years, $\sim$18.6 years (lunar nodal cycle), $\sim$33 years, and $\sim$60 years. We discuss these results in the framework of Laplace's theory, with a particular focus on the phase relationships between seismicity, length-of-day variations, and sea-level changes to further elucidate the underlying physical mechanisms. Finally,integrating observations from seismogenic regions, we propose a trigger mechanism based on solid Earth-hydrosphere interactions, emphasizing the key role of water-rock interactions in modulating earthquake occurrence.

Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth System Models (ESMs) face similar challenges, operating at high spatial and temporal resolutions, producing petabytes of data per simulated day. Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates due to their abundance of unlabeled data. In this review, we outline recent developments in NC applied to geospatial data. We introduce the fundamental concepts of NC including seminal works in its traditional applications to image and video compression domains with focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, we review current applications of NC across various EO modalities and explore the limited efforts in ESM compression to date. The advent of self-supervised learning (SSL) and foundation models (FM) has advanced methods to efficiently distill representations from vast unlabeled data. We connect these developments to NC for EO, highlighting the similarities between the two fields and elaborate on the potential of transferring compressed feature representations for machine--to--machine communication. Based on insights drawn from this review, we devise future directions relevant to applications in EO and ESM.

Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. Despite many years of advancements, 3D inversion still poses challenges in terms of computational requirements, regularization selection, hyperparameter tuning and real-time inversion. We present a new approach for the inversion of airborne electromagnetic data that leverages machine learning to overcome the computational burden of traditional 3D inversion methods, which implicitly includes learned regularization and is applicable in real-time. The method combines 1D inversion results with geostatistical modeling to create tailored training datasets, enabling the development of a specialized neural network that predicts 2D conductivity models from airborne electromagnetic data. This approach requires 3D forward modeling and 1D inversion up front, but no forward modeling during inference. The workflow is applied to the Kaweah Subbasin in California, where it successfully reconstructs conductivity models consistent with real-world data and geological drill hole information. The results highlight the method's capability to deliver fast and accurate subsurface imaging, offering a valuable tool for groundwater exploration and other near-surface applications.

Carbonate minerals are important in Earth's system sciences and have been found on Mars and in meteorites and asteroids, highlighting the importance of impacts in planetary processes. While extensively studied under static compression, the behavior of carbonates under shock compression remains underexplored, with no $\textit{in situ}$ X-ray investigations reported so far. Here we investigate natural magnesiosiderite (Fe$_{0.6}$Mg$_{0.4}$CO$_{3}$) under nanosecond laser-driven shock compression at pressures up to 150 GPa, coupled with $\textit{in situ}$ ultrafast synchrotron X-ray absorption spectroscopy (XAS). The interpretation of the experimental spectra is complemented using first-principles absorption cross-section calculations performed on crystalline phases at different pressures and on a dense liquid phase obtained using density functional theory-based molecular dynamics (DFT-MD) simulations. Under laser-driven shock compression, the magnesiosiderite crystal phase remains unchanged up to the melt. Under shock reverberation, the absorption spectra show changes similar to those attributed to a high-spin to low-spin transition observed under static compression. At higher pressures, the laser shock induces the formation of CO$_4$ tetrahedral units in the melt. Upon unloading from the shocked state, only a few nanoseconds later, the original magnesiosiderite phase is recovered.

Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observed and predicted seismic data. However, conventional FWI encounters challenges in real data applications, primarily due to its conventional objective of direct measurements of the data misfit. Accurate estimation of the source wavelet is essential for effective data fitting, alongside the need for low-frequency data and a reasonable initial model to prevent cycle skipping. Additionally, wave equation solvers often struggle to accurately simulate the amplitude of observed data in real applications. To address these challenges, we introduce a correlation-based source-independent objective function for FWI that aims to mitigate source uncertainty and amplitude dependency, which effectively enhances its practicality for real data applications. We develop a deep-learning framework constrained by this new objective function with a velocity-distribution supported deep image prior, which reparameterizes velocity inversion into trainable parameters within an autoencoder, thereby reducing the nonlinearity in the conventional FWI's objective function. We demonstrate the superiority of our proposed method using synthetic data from benchmark velocity models and, more importantly, two real datasets. These examples highlight its effectiveness and practicality even under challenging conditions, such as missing low frequencies, a crude initial velocity model, and an incorrect source wavelet.

The constant temperature and constant heat flux thermal boundary conditions, both developing distinct flow patterns, represent limiting cases of ideally conducting and insulating plates in Rayleigh-B\'enard convection (RBC) flows, respectively. This study bridges the gap in between, using a conjugate heat transfer (CHT) set-up and studying finite thermal diffusivity ratios $\kapparatioIL$ to better represent real-life conditions in experiments. A three-dimensional RBC configuration including two fluid-confining plates is studied via direct numerical simulation given a Prandtl number $\Pr=1$. The fluid layer of height $H$ and horizontal extension $L$ obeys no-slip boundary conditions at the two solid-fluid interfaces and an aspect ratio of $\Gamma=L/H=30$, while the relative thickness of each plate is $\Gs=H_s/H=15$. The entire domain is laterally periodic. Here, different $\kapparatioIL$ are investigated for Rayleigh numbers of $\Ra=\left\{ 10^4, 10^5 \right\}$. We observe a gradual shift of the size of the characteristic flow patterns and their induced heat and mass transfer as $\kapparatioIL$ is varied, suggesting a relation between the recently studied turbulent superstructures and supergranules for constant temperature and constant heat flux boundary conditions, respectively. Performing a linear stability analysis for this CHT configuration confirms these observations theoretically while extending previous studies by investigating the impact of a varying solid plate thickness $\Gs$. Moreover, we study the impact of $\kapparatioIL$ on the thermal and viscous boundary layers. Given the prevalence of finite $\kapparatioIL$ in nature, this work also extends our understanding of pattern formation in geo- and astrophysical convection flows.

The metastable phases can lead to multistep nucleation processes, influencing the liquid-to-solid transition in various systems. In this study, we investigate the homogeneous nucleation of iron's crystalline phases under Earth's inner core conditions, employing two previously developed interatomic potentials. We compare the thermodynamic and kinetic properties of iron relevant to the nucleation as predicted by these potentials. While the potentials differ in their predictions of melting temperature by a few hundred Kelvins, they show a consistent description of the relative Gibbs free energy between solid and liquid phases with respect to the undercooling. Both potentials also predict that the metastable bcc phase exhibits a significantly higher nucleation rate than the hcp phase over a wide range of undercooling. This substantially lowers the conditions required for the initial nucleation of Earth's inner core. The results validate the commonality of the two-step nucleation mechanism of iron under Earth's inner core conditions for two different potentials, providing a foundation for future studies about the influence of other elements on the nucleation of Earth's core.

High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sensor-Invariant Tactile Representations (SITR), enabling zero-shot transfer across optical tactile sensors. Our approach utilizes a transformer-based architecture trained on a diverse dataset of simulated sensor designs, allowing it to generalize to new sensors in the real world with minimal calibration. Experimental results demonstrate the method's effectiveness across various tactile sensing applications, facilitating data and model transferability for future advancements in the field.

This paper presents a comparative analysis of structural seismic responses under two types of ground motion inputs: (i) synthetic motions generated by stochastic ground motion models and (ii) recorded motions from an earthquake database. Five key seismic response metrics - probability distributions, statistical moments, correlations, tail indices, and variance-based global sensitivity indices - are systematically evaluated for two archetypal structures: a 12-story medium-period building and a high-rise long-period tower. Both ground motion datasets are calibrated to a shared response spectrum, ensuring consistency in spectral characteristics, including spectral median, variance, and correlation structure. The analysis incorporates both aleatory uncertainties from ground motion variability and epistemic uncertainties associated with structural parameters, providing a comprehensive comparison of seismic responses. The results demonstrate close agreement in global response characteristics, including distributions, correlations, and sensitivity indices, between synthetic and recorded motions, with differences typically within 15\%. However, significant discrepancies are observed under extreme conditions, particularly in tail behavior, higher-order moments, and drift responses of long-period structures, with differences exceeding 50\%. These discrepancies are attributed to the non-Gaussian features and complex characteristics inherent in recorded motions, which are less pronounced in synthetic datasets. The findings support the use of synthetic ground motions for evaluating global seismic response characteristics, while highlighting their limitations in capturing rare-event behavior and long-period structural dynamics.

Among the biogenic macroelements, phosphorus is the one bringing the most fascinating and unsolved mysteries for what concern its prebiotic history. It possibly landed on Earth as a metal phosphide (Schreibersite, (Fe,Ni)3P), throughout the Heavy Meteor Bombardment during the Archean Era. Its subsequent corrosion by water led to P-oxygenated compounds, which is the subject of this kinetic computational study, thus complementing our previous thermodynamic characterization. The reaction was studied at periodic DFT level, simulating the water corrosion reaction on the reactive Fe2NiP Schreibersite (001)2 surface. Results show that the timescale of the reaction at 350 K is of few hours.

The IceCube Neutrino Observatory is an optical Cherenkov detector instrumenting one cubic kilometer of ice at the South Pole. The Cherenkov photons emitted following a neutrino interaction are detected by digital optical modules deployed along vertical strings within the ice. The densely instrumented bottom central region of the IceCube detector, known as DeepCore, is optimized to detect GeV-scale atmospheric neutrinos. As upward-going atmospheric neutrinos pass through Earth, matter effects alter their oscillation probabilities due to coherent forward scattering with ambient electrons. These matter effects depend upon the energy of neutrinos and the density distribution of electrons they encounter during their propagation. Using simulated data at the IceCube Deepcore equivalent to its 9.3 years of observation, we demonstrate that atmospheric neutrinos can be used to probe the broad features of the Preliminary Reference Earth Model. In this contribution, we present the preliminary sensitivities for establishing the Earth matter effects, validating the non-homogeneous distribution of Earth's electron density, and measuring the mass of Earth. Further, we also show the DeepCore sensitivity to perform the correlated density measurement of different layers incorporating constraints on Earth's mass and moment of inertia.

Auroral streamers are important meso-scale processes of dynamic magnetosphere-ionosphere coupling, typically studied using imagers sensitive to energetic (>1 keV) electron precipitation, such as all-sky imagers (ASIs). This paper reports streamer-like red-line auroras, representing low-energy (<1 keV) precipitation, observed poleward of a black aurora and an auroral torch. These red-line auroras were associated with a magnetospheric electron injection and braking ion flows. Observations were made using the THEMIS spacecraft and ground-based imagers, including the ASI, REGO, and meridian scanning photometer (MSP) at Fort Smith. We identify plasma sheet electron pitch-angle scattering by time-domain structures (TDSs) and electron cyclotron harmonics (ECH) waves as the driver of these red-line auroras, because of (1) a strong correlation (~0.9) between observed red-line intensities and precipitating fluxes; (2) consistent red-line intensities from auroral transport code forward modeling, and (3) consistent precipitation characteristic energies from MSP optical inference and quasi-linear estimates.

Extensive monitoring of acoustic activities is important for many fields, including biology, security, oceanography, and Earth science. Distributed acoustic sensing (DAS) is an evolving technique for continuous, wide-coverage measurements of mechanical vibrations, which is suited to ocean applications. DAS illuminates an optical fiber with laser pulses and measures the backscattered wave due to small random variations in the refractive index of the material. Specifically, DAS uses coherent optical interferometry to measure the phase difference of the backscattered wave from adjacent locations along the fiber. External stimuli, such as mechanical strain due to acoustic wavefields impinging on the fiber-optic cable, modulate the backscattered wave. Hence, the differential phase measurements of the optical backscatter are proportional to the underlying physical quantities of the surrounding wavefield. Continuous measurement of the backscattered electromagnetic signal provides a distributed sensing modality that extends spatially along the fiber. We provide an overview of DAS technology and detail the underlying physics, from electromagnetic to mechanical and eventually acoustic quantities. We explain the effect of DAS acquisition parameters in signal processing and show the potential of DAS for sound source detection on data collected from the Ocean Observatories Initiative Regional Cabled Array https://doi.org/10.58046/5J60-FJ89.