Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey explores active tectonics in Northeastern Caribbean

There is renewed interest in studying the active and complex northeastern Caribbean plate boundary to better understand subduction zone processes and for earthquake and tsunami hazard assessments [e.g., ten Brink and Lin, 2004; ten Brink et al., 2004; Grindlay et al, 2005]. To study the active tectonics of this plate boundary, the GEOPRICO-DO (Geological, Puerto Rico-Dominican) marine geophysical cruise, carried out between 28 March and 17 April 2005 (Figure 1), studied the active tectonics of this plate boundary. Initial findings from the cruise have revealed a large underwater landslide, and active faults on the seafloor (Figures 2a and 2c). These findings indicate that the islands within this region face a high risk from tsunami hazards, and that local governments should be alerted in order to develop and coordinate possible mitigation strategies. The cruise collected multibeam bathymetry, gravity, magnetic, high-resolution seismic, deep seismic sounding, and multichannel seismic reflection data, which are currently being processed and interpreted (Table 1). In early November 2005, 10 ocean-bottom seismometers (OBS) that had been deployed northeast of Puerto Rico and the Virgin Islands (Figure 1) during the cruise were recovered. These OBS recorded data during the cruise and the local seismicity between April and October 2005.
Twist Angle Dependence of Exciton Resonances in WSe$_2$/MoSe$_2$ Moir\'e Heterostructures

Van der Waals heterostructures based on TMDC semiconducting materials have emerged as promising materials due to their spin-valley properties efficiently contrived by the stacking-twist angle. The twist angle drastically alters the interlayer excitonic response by determining the spatial modulation, confining moir\'e potential, and atomic reconstruction in those systems. Nonetheless, the impact of the interlayer twist angle on the band alignment of the monolayers composing the heterostructure has received scant attention in the current research. Here, we systematically investigate the twist-angle dependence of intra- and inter-layer excitons in twisted WSe2/MoSe2 heterobilayers. By performing photoluminescence excitation spectroscopy, we identify the twist-angle dependence of interlayer emission response, where an energy redshift of about 100 meV was observed for increasing twist angles. The applied microscopic theory predicts, on the contrary, a blueshift, which suggests that additional features, such as atomic reconstruction, may also surpass the moir\'e potential confinement. Those findings also prompt the effects of dielectric screening by addressing the redshift response to the stacking layer order. Furthermore, our findings support the evidence of a band offset dependence on the twist angle for the adjacent monolayers composing the heterobilayer system. Our fundamental study of exciton resonances deepens the current understanding of the physics of twisted TMDC heterostructures and paves the way for future experiments and theoretical works.
HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field

The problem of modeling an animatable 3D human head avatar under lightweight setups is of significant importance but has not been well solved. Existing 3D representations either perform well in the realism of portrait images synthesis or the accuracy of expression control, but not both. To address the problem, we introduce a novel hybrid explicit-implicit 3D representation, Facial Model Conditioned Neural Radiance Field, which integrates the expressiveness of NeRF and the prior information from the parametric template. At the core of our representation, a synthetic-renderings-based condition method is proposed to fuse the prior information from the parametric model into the implicit field without constraining its topological flexibility. Besides, based on the hybrid representation, we properly overcome the inconsistent shape issue presented in existing methods and improve the animation stability. Moreover, by adopting an overall GAN-based architecture using an image-to-image translation network, we achieve high-resolution, realistic and view-consistent synthesis of dynamic head appearance. Experiments demonstrate that our method can achieve state-of-the-art performance for 3D head avatar animation compared with previous methods.
The strongest aftershock in seismic models of epidemic type

We consider an epidemic-type aftershock model, ETAS(F), for a large class of distributions F determining the number of direct aftershocks. This class includes Poisson, Geometric, Negative Binomial distributions and many other. Assuming an exponential form of the productivity and magnitude laws, we find a limiting distribution of the strongest aftershock magnitude μa when the initial cluster event m• is large. The regime can be either subcritical or critical; the initial event can be dominant in size or not. In the subcritical regime, the mode of the limiting distribution is determined by the parameters of productivity and the magnitude laws; the shape of this distribution is not universal and is effectively determined by F. For example, the Geometric F-distribution generates the logistic law, and the Poisson distribution (studied earlier) generates the Gumbel type 1 law. The accuracy of these laws for moderate initial magnitudes is tested numerically. The limit distribution of the Båth’s difference m• − μa is independent of the initial event size only if the regime is critical, and the ratio of exponents in the laws of magnitude and productivity is contained in the interval (1,2). Previous studies of the μa-distribution have dealt with the traditional Poisson F model and with arbitrary (not necessarily dominant) initial magnitude m•.
Energy minimization of paired composite fermion wave functions in the spherical geometry

We perform the energy minimization of the paired composite fermion (CF) wave functions, proposed by M\"oller and Simon (MS) [PRB 77, 075319 (2008)] and extended by Yutushui and Mross (YM) [PRB 102, 195153 (2020)], where the energy is minimized by varying the CF pairing function, in the case of an approximate model of the Coulomb interaction in the second Landau level for pairing channels $\ell = -1, 3, 1$ which are expected to be in the Pfaffian, anti-Pfaffian and particle-hole symmetric (PH) Pfaffian phases respectively. It is found that the energy of the $\ell = -1$ MS wave function can be reduced substantially below that of the Moore-Read wave function at small system sizes, however, in the $\ell = 3$ case the energy cannot be reduced much below that of the YM trial wavefunction. Nonetheless, both our optimized and unoptimized wavefunctions with $\ell=-1,3$ extrapolate to roughly the same energy per particle in the thermodynamic limit. For the $\ell = 1$ case, the optimization makes no qualitative difference and these PH-Pfaffian wave functions are still energetically unfavourable. The effective CF pairing is analyzed in the resulting wave functions, where the effective pairing for the $\ell = -1, 3$ channels is found to be well approximated by a weak-pairing BCS ansatz and the $\ell = 1$ wave functions show no sign of emergent CF pairing.
Too Big, so Fail? - Enabling Neural Construction Methods to Solve Large-Scale Routing Problems

In recent years new deep learning approaches to solve combinatorial optimization problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed. The most impactful of these methods are sequential neural construction approaches which are usually trained via reinforcement learning. Due to the high training costs of these models, they usually are trained on limited instance sizes (e.g. serving 100 customers) and later applied to vastly larger instance size (e.g. 2000 customers). By means of a systematic scale-up study we show that even state-of-the-art neural construction methods are outperformed by simple heuristics, failing to generalize to larger problem instances. We propose to use the ruin recreate principle that alternates between completely destroying a localized part of the solution and then recreating an improved variant. In this way, neural construction methods like POMO are never applied to the global problem but just in the reconstruction step, which only involves partial problems much closer in size to their original training instances. In thorough experiments on four datasets of varying distributions and modalities we show that our neural ruin recreate approach outperforms alternative forms of improving construction methods such as sampling and beam search and in several experiments also advanced local search approaches.
Evidence of electrical anisotropy in limestone formations using the RMT technique

Azimuthal resistivity surveys are often applied to complement hydrological information or to improve the location of observation boreholes in pump tests. Symmetric electrode configurations cannot distinguish anisotropy from lateral changes or dipping layers, but asymmetric arrays (e.g., the offset Wenner array) can. Tensor radiomagnetotellurics (RMT) is presented as an alternative method in studies of electrical anisotropy in the shallow subsurface. The electromagnetic and geomagnetic transfer functions provide information about the dimensionality of the data. These transfer functions can also be used to find the directions of anisotropy. Data with an anisotropic signature can be inverted for a one‐dimensional (1D) azimuthal anisotropy model. The method is faster than the azimuthal resistivity method.A 380‐m‐long profile of tensor RMT data (12.7–243 kHz) from limestones that overlie shale on the island of Gotland, Sweden, is used to show the merits of the method. The data have a clear anisotropic signatur.
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features

In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking. Yet in practice, despite their theoretical advantages, the body of work on event-based multi-object tracking (MOT) remains in its infancy, especially in real-world settings where events from complex background and camera motion can easily obscure the true target motion. In this work, an event-based multi-object tracker, called SpikeMOT, is presented to address these challenges. SpikeMOT leverages spiking neural networks to extract sparse spatiotemporal features from event streams associated with objects. The resulting spike train representations are used to track the object movement at high frequency, while a simultaneous object detector provides updated spatial information of these objects at an equivalent frame rate. To evaluate the effectiveness of SpikeMOT, we introduce DSEC-MOT, the first large-scale event-based MOT benchmark incorporating fine-grained annotations for objects experiencing severe occlusions, frequent trajectory intersections, and long-term re-identification in real-world contexts. Extensive experiments employing DSEC-MOT and another event-based dataset, named FE240hz, demonstrate SpikeMOT's capability to achieve high tracking accuracy amidst challenging real-world scenarios, advancing the state-of-the-art in event-based multi-object tracking.
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models (Vision Paper)

In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlighted the potential of using foundation models in geospatial artificial intelligence, known as GeoAI Foundation Models, for geographic question answering, remote sensing image understanding, map generation, and location-based services, among others. However, the development and application of GeoAI foundation models can pose serious privacy and security risks, which have not been fully discussed or addressed to date. This paper introduces the potential privacy and security risks throughout the lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for research directions and preventative and control strategies. Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.
APPLICATION OF A SPATIAL VERIFICATION METHOD TO GFS PRECIPITATION FORECASTS

BSTRACT. This study aims to describe a spatial analysis of precipitation field with the MODE tool, which consists in comparing features converted from griddedforecast and observed precipitation values. This evaluation was performed daily from April 2010 to March 2011, for the 36-h GFS precipitation forecast started at00 UTC over the state of Sao Paulo and neighborhood. Besides traditional verification measures, such as accuracy (A), critical success index (CSI), bias (BIAS),probability of detection (POD), and false alarm ratio (FAR); new verification measures are proposed, such as area ratio (AR), centroid distance (CD) and 50th and 90thpercentiles ratio of intensity (PR50 and PR90). Better performance was attained during the rainy season. Part of the errors in the simulations was due to overestimationof the forecasted intensity and precipitation areas. Keywords : object-based verification, weather forecast, precipitation, MODE, Sao Paulo. RESUMO. Este estudo tem como objetivo descrever uma analise espacial do campo de precipitacao com a ferramenta MODE, que consiste em converter valores deprecipitacao de grade do campo previsto e observado em objetos, que posteriormente serao comparados entre si. A avaliacao e realizada diariamente sobre o estadode Sao Paulo e vizinhanca, para o periodo de abril de 2010 a marco de 2011, para as simulacoes do modelo GFS iniciadas as 00 UTC, na integracao de 36 horas. Alem da verificacao atraves de indices tradicionais, como probabilidade de acerto (PA), indice critico de sucesso (ICS), vies (VIES), probabilidade de deteccao (PD)e razao de falso alarme (RFA), novos indices de avaliacao sao propostos, como razao de area (RA), distância do centroide (DC) e razao dos percentis 50 e 90 deintensidade (RP50 e RP90). O melhor desempenho ocorreu para a estacao chuvosa. Parte dos erros nas simulacoes foi devido a superestimativa da intensidade e da area de abrangencia dos eventos de precipitacao em relacao ao observado. Palavras-chave: avaliacao baseada em objetos, previsao do tempo, precipitacao, MODE, Sao Paulo.
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning

The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
Meta-Path Learning for Multi-relational Graph Neural Networks

Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work we propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments.
Comparisons of remote sensing retrievals and in situ measurements of aerosol fine mode fraction during ACE‐Asia

We present sunphotometer‐retrieved and in situ fine mode fractions (FMF) measured onboard the same aircraft during the ACE‐Asia experiment. Comparisons indicate that the latter can be used to identify whether the aerosol under observation is dominated by a mixture of modes or a single mode. Differences between retrieved and in situ FMF range from 5–20%. When profiles contained multiple layers of aerosols, the retrieved and measured FMF were segregated by layers. The comparison of layered and total FMF from the same profile indicates that columnar values are intermediate to those derived from layers. As a result, a remotely sensed FMF cannot be used to distinguish whether the aerosol under observation is composed of layers each with distinctive modal features or all layers with the same modal features. Thus, the use of FMF in multiple layer environments does not provide unique information on the aerosol under observation.
MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)

Large language models (LLMs) are advancing rapidly. Such models have demonstrated strong capabilities in learning from large-scale (unstructured) text data and answering user queries. Users do not need to be experts in structured query languages to interact with systems built upon such models. This provides great opportunities to reduce the barrier of information retrieval for the general public. By introducing LLMs into spatial data management, we envisage an LLM-based spatial database system to learn from both structured and unstructured spatial data. Such a system will offer seamless access to spatial knowledge for the users, thus benefiting individuals, business, and government policy makers alike.
Grain boundary segregation and phase separation in ceria-zirconia from atomistic simulation

Doping is the most common strategy employed in the development of new and improved materials. However, predicting the effects of doping on the atomic-scale structure of a material is often difficult or limited to high-end experimental techniques. Doping can induce phase separation in a material, undermining the material's stability. A further complication is that dopant atoms can segregate to interfaces in a material such as grain boundaries (GBs), with consequences for key macroscopic properties of the material such as its conductivity. Here, we describe a computational methodology based on semi-grand canonical Monte Carlo which can be used to probe these phenomena at the atomic scale for metal oxide solid solutions. The methodology can provide precise predictions of the thermodynamic conditions at which phase separation occurs. It can also provide the segregation patterns exhibited by GBs at given conditions. We apply the methodology to one of the most important catalytic materials, ceria-zirconia. Our calculations reveal an interesting richness in the GB segregation in this system. Most GBs we examined exhibited continuous increases in Zr segregation upon Zr doping, with a concomitant reduction in the formation enthalpies of the GBs. However, a few GBs exhibited no segregation at low temperatures. We also observed evidence of first-order complexion transitions in some GBs.
AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback

Large Language Models (LLMs) have demonstrated significant success across various domains. However, their application in complex decision-making tasks frequently necessitates intricate prompt engineering or fine-tuning, leading to challenges in unseen downstream tasks and heavy demands on computational resources. Meanwhile, Reinforcement Learning (RL) has been recognized as effective in decision-making problems but struggles in environments with sparse rewards, such as open-world games. To overcome these challenges, we introduce AdaRefiner, a novel framework designed to enhance the synergy between LLMs and RL feedback. The key component of AdaRefiner is a lightweight Adapter Language Model (LM), which automatically refines task comprehension based on feedback from RL agents. This method mitigates the need for intricate prompt engineering and intensive LLM fine-tuning while maintaining the LLMs' generalization abilities and enhancing their decision-making capabilities in downstream tasks. Empirical evaluations of AdaRefiner on 22 diverse tasks within the open-world game Crafter have demonstrated its superior effectiveness, especially in guiding agents towards higher-level and common-sense skills. Our work makes contributions to the automatic self-refinement of LLMs with RL feedback, offering a more adaptable and efficient solution for complex decision-making problems.
Estimating sampling uncertainty in fixed‐depth and fixed‐isotherm estimates of ocean warming

We present a simple, generic method for parameterising the sampling and measurement uncertainty for time series data that is based on the number of observations that constitutes each data point. The method is demonstrated using basin‐averaged monthly time series of (1) the mean temperature above 220 m depth (T220m); (2) the mean temperature above the 14 °C isotherm (T14C) and (3) the depth of the 14 °C isotherm (D14C). The T220m and T14C analyses are in some sense ‘equivalent’, since the spatial‐time‐mean depth of the 14 °C isotherm is approximately 220 m. In agreement with oceanographic theory, we find that the T14C time series has a consistently lower associated sampling uncertainty than T220m for all ocean basins considered in this study. We also note that the sampling uncertainty for all quantities for a given number of observations is dependent on ocean basin. Our results suggest that with the current observing array we are able to monitor global monthly values of T220m, T14C and D14C with a measurement and sampling uncertainty of approximately ± 0.07 °C, ± 0.04 °C and ± 2.4 m, respectively. However, these estimated uncertainties will become much larger on moving to regional or smaller scales. © Crown Copyright 2010. Published by John Wiley & Sons Ltd.
Reevaluation of the oxygen isotopic composition of planktonic foraminifera: Experimental results and revised paleotemperature equations

Cultured planktonic foraminifera, Orbulina universa (symbiotic) and Globigerina bulloides (nonsymbiotic), are used to reexamine temperature:δ18O relationships at 15°–25°C. Relationships for both species can be described by linear equations. Equations for O. universa grown under low light (LL) and high light (HL) share a slope of −4.80 (0.21‰ °C−1) with a HL-LL offset of −0.33‰ due to symbiont photosynthetic activity. The effect of [CO32−] on O. universa is −0.002‰ µmol−1 kg−1 and is insensitive to temperature. For G. bulloides, ontogenetic effects produce size-related trends in temperature:δ18O, whereby larger shells are enriched in 18O relative to smaller specimens. The O. universa temperature:δ18O relationships are more accurate than previously published equations for describing plankton tow data. Our equations do not explain planktonic core top data with the same precision but provide a good fit to benthic Cibicidoides data below 10°C. Temperature:δ18O relationships for G. bulloides provide good agreement with field data for this species from the northeast Pacific.