Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling finite-fault radiation from the ωn spectrum

The high-frequency seismic field near the epicenter of a large earthquake is modeled by subdividing the fault plane into subelements and summing their contributions at the observation point. Each element is treated as a point source with an ω2 spectral shape, where ω is the angular frequency. Ground-motion contributions from the subsources are calculated using a stochastic model. Attenuation is based on simple geometric spreading in a whole space, coupled with regional anelastic attenuation (Q operator). The form of the ωn spectrum with natural n follows from point shear-dislocation theory with an appropriately chosen slip time function. The seismic moment and corner frequency are the two parameters defining the point-source spectrum and must be linked to the subfault size to make the method complete. Two coefficients, Δσ and K, provide this link. Assigning a moment to a subfault of specified size introduces the stress parameter, Δσ. The relationship between corner frequency (dislocation growth rate) and fault size is established through the coefficient K, which is inherently nonunique. These two parameters control the number of subsources and the amplitudes of high-frequency radiation, respectively. Derivation of the model from shear-dislocation theory reveals the unavoidable uncertainty in assigning ωn spectrum to faults with finite size. This uncertainty can only be reduced through empirical validation. The method is verified by simulating data recorded on rock sites near epicenters of the M8.0 1985 Michoacan (Mexico), the M8.0 1985 Valparaíso (Chile), and the M5.8 1988 Saguenay (Québec) earthquakes. Each of these events is among the largest for which strong-motion records are available, in their respective tectonic environments. The simulations for the first two earthquakes are compared to the more detailed modeling of Somerville et al. (1991), which employs an empirical source function and represents the effects of crustal structure using the theoretical impulse response. Both methods predict the observations accurately on average. The precision of the methods is also approximately equal; the predicted acceleration amplitudes in our model are generally within 15% of observations. An unexpected result of this study is that a single value of a parameter K provides a good fit to the data at high frequencies for all three earthquakes, despite their different tectonic environments. This suggests a simplicity in the modeling of source processes that was unanticipated.
ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis

In this work, we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with the Score Distillation Sampling (SDS) method, to lift the 2D image into 3D space. However, a generalizable implicit field often results in an over-smooth texture field, while the SDS method tends to lead to a texture-inconsistent novel view with the input image. In this paper, we introduce a texture-consistent back view synthesis module that could transfer the reference image content to the back view through depth and text-guided attention injection. Moreover, to alleviate the color distortion that occurs in the side region, we propose a visibility-aware patch consistency regularization for texture mapping and refinement combined with the synthesized back view texture. With the above techniques, we could achieve high-fidelity and texture-consistent human rendering from a single image. Experiments conducted on both real and synthetic data demonstrate the effectiveness of our method and show that our approach outperforms previous baseline methods.
A Stochastic Non-Zero-Sum Game of Controlling the Debt-to-GDP Ratio

We introduce a non-zero-sum game between a government and a legislative body to study the optimal level of debt. Each player, with different time preferences, can intervene on the stochastic dynamics of the debt-to-GDP ratio via singular stochastic controls, in view of minimizing non-continuously differentiable running costs. We completely characterise Nash equilibria in the class of Skorokhod-reflection-type policies. We highlight the importance of different time preferences resulting in qualitatively different type of equilibria. In particular, we show that, while it is always optimal for the government to devise an appropriate debt issuance policy, the legislator should optimally impose a debt ceiling only under relatively low discount rates and a laissez-faire policy can be optimal for high values of the legislator's discount rate.
Improving Stability during Upsampling - on the Importance of Spatial Context

State-of-the-art models for pixel-wise prediction tasks such as image restoration, image segmentation, or disparity estimation, involve several stages of data resampling, in which the resolution of feature maps is first reduced to aggregate information and then sequentially increased to generate a high-resolution output. Several previous works have investigated the effect of artifacts that are invoked during downsampling and diverse cures have been proposed that facilitate to improve prediction stability and even robustness for image classification. However, equally relevant, artifacts that arise during upsampling have been less discussed. This is significantly relevant as upsampling and downsampling approaches face fundamentally different challenges. While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling. Blurring is therefore not an option and dedicated operations need to be considered. In this work, we are the first to explore the relevance of context during upsampling by employing convolutional upsampling operations with increasing kernel size while keeping the encoder unchanged. We find that increased kernel sizes can in general improve the prediction stability in tasks such as image restoration or image segmentation, while a block that allows for a combination of small-size kernels for fine details and large-size kernels for artifact removal and increased context yields the best results.
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery

Geographic location is essential for modeling tasks in fields ranging from ecology to epidemiology to the Earth system sciences. However, extracting relevant and meaningful characteristics of a location can be challenging, often entailing expensive data fusion or data distillation from global imagery datasets. To address this challenge, we introduce Satellite Contrastive Location-Image Pretraining (SatCLIP), a global, general-purpose geographic location encoder that learns an implicit representation of locations from openly available satellite imagery. Trained location encoders provide vector embeddings summarizing the characteristics of any given location for convenient usage in diverse downstream tasks. We show that SatCLIP embeddings, pretrained on globally sampled multi-spectral Sentinel-2 satellite data, can be used in various predictive tasks that depend on location information but not necessarily satellite imagery, including temperature prediction, animal recognition in imagery, and population density estimation. Across tasks, SatCLIP embeddings consistently outperform embeddings from existing pretrained location encoders, ranging from models trained on natural images to models trained on semantic context. SatCLIP embeddings also help to improve geographic generalization. This demonstrates the potential of general-purpose location encoders and opens the door to learning meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data.
Evaluation of the SMHI coupled atmosphere-ice-ocean model RCA4_NEMO

Den regionala kopplade atmosfar-is-havsmodellen RCA4-NEMO som utvecklats vid SMHI, utvarderas baserat pa en ERA40-ateranalys. Utvecklingen av den regionala klimatmodellen fortsatter men en forsta u .
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution has attained widespread prominence to enhance the spatial resolution of hyperspectral images. However, convolution-based methods have encountered challenges in harnessing the global spatial-spectral information. The prevailing transformer-based methods have not adequately captured the long-range dependencies in both spectral and spatial dimensions. To alleviate this issue, we propose a novel cross-scope spatial-spectral Transformer (CST) to efficiently investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. By integrating global information into the rectangle-window self-attention, we first design a cross-scope spatial self-attention to facilitate long-range spatial interactions. Then, by leveraging appropriately characteristic spatial-spectral features, we construct a cross-scope spectral self-attention to effectively capture the intrinsic correlations among global spectral bands. Finally, we elaborate a concise feed-forward neural network to enhance the feature representation capacity in the Transformer structure. Extensive experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually. The code is available at \url{https://github.com/Tomchenshi/CST.git}.
Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography

Historical buildings are a treasure and milestone of human cultural heritage. Reconstructing the 3D models of these building hold significant value. The rapid development of neural rendering methods makes it possible to recover the 3D shape only based on archival photographs. However, this task presents considerable challenges due to the limitations of such datasets. Historical photographs are often limited in number and the scenes in these photos might have altered over time. The radiometric quality of these images is also often sub-optimal. To address these challenges, we introduce an approach to reconstruct the geometry of historical buildings, employing volumetric rendering techniques. We leverage dense point clouds as a geometric prior and introduce a color appearance embedding loss to recover the color of the building given limited available color images. We aim for our work to spark increased interest and focus on preserving historical buildings. Thus, we also introduce a new historical dataset of the Hungarian National Theater, providing a new benchmark for the reconstruction method.
The Common Envelope Evolution Outcome. II. Short-orbital-period Hot Subdwarf B Binaries Reveal a Clear Picture

Common envelope evolution (CEE) is vital for forming short-orbital-period compact binaries. It covers many objects, such as double compact merging binaries, Type Ia supernovae progenitors, binary pulsars, and X-ray binaries. Knowledge of the common envelope (CE) ejection efficiency still needs to be improved, though progress has been made recently. Short-orbital-period hot subdwarf B star (sdB) plus white dwarf (WD) binaries are the most straightforward samples with which to constrain CEE physics. We apply the known orbital period–WD mass relation to constrain the sdB progenitors of seven sdB+WD binaries with a known inclination angle. The average CE efficiency parameter is 0.32. This is consistent with previous studies. However, the CE efficiency need not be constant, but a function of the initial mass ratio, based on well-constrained sdB progenitor mass and evolutionary stage. Our results can be used as physical inputs for binary population synthesis simulations of related objects. A similar method can also be applied to study other short-orbital-period WD binaries.
Tropical patterns from the Fock-Goncharov pairing and Ringel's conjectures

We study tropical friezes and cluster-additive functions associated to symmetrizable generalized Cartan matrices in the framework of Fock-Goncharov duality in cluster algebras. As a special case, we generalize and prove two conjectures of C. M. Ringel on cluster-additive functions for arbitrary Cartan matrices of finite type.
The nature of static and dynamic correlation in nearly degenerate singlet and triplet states of N-heterocyclic chromophores

In this paper we investigate the role of electron correlation in predicting the S$_1$-S$_0$ and T$_1$-S$_0$ excitation energies and hence, the singlet-triplet gap ($\Delta$E$_{ST}$) in a set of cyclazines which act as templates for potential candidates for 5th generation Organic Light Emitting Diode (OLED) materials. This issue has recently garnered much interest with the focus being on the inversion of the $\Delta$E$_{ST}$, although experiments have indicated near degenerate levels with both positive and negative being within the experimental error bar (J. Am. Chem. Soc., 102: 6068 , J. Am. Chem. Soc., 108: 17 ). We have carried out a systematic and exhaustive study of various excited state electronic structure methodologies and identified the strengths and shortcomings of the various approaches and approximations in view of this challenging case. We have found that near degeneracy can be achieved either with a proper balance of static and dynamic correlation in multireference theories or with state-specific orbital corrections including its coupling with correlation. The role of spin contamination is also discussed. Eventually, this paper seeks to produce benchmark numbers for establishing cheaper theories which can then be used for screening derivatives of these templates with desirable optical and structural properties. Additionally we would like to point out that the use of DLPNO-STEOM-CCSD as the benchmark for $\Delta$E$_{ST}$ (as used in J. Phys. Chem. A, 126: 8: 1378, Chem. Phys. Lett., 779: 138827) is not a suitable benchmark for this class of molecules.
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth

TodevelopanovelUncertaintyQuantification (UQ) framework to estimate the uncertainty of patient survival models in the absence of ground truth, we developed and evaluated our approach based on a dataset of 1383 patients treated with stereotactic radiosurgery (SRS) for brain metastases between January 2015 and December 2020. Our motivating hypothesis is that a time-to-event prediction of a test patient on inference is more certain given a higher feature-space-similarity to patients in the training set. Therefore, the uncertainty for a particular patient-of-interest is represented by the concordance index between a patient similarity rank and a prediction similarity rank. Model uncertainty was defined as the increased percentage of the max uncertainty-constrained-AUC compared to the model AUC. We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR). Our results show that all models had the lowest uncertainty on ICP (2.21%) and the highest uncertainty (17.28%) on ICPD. OS models demonstrated high variation in uncertainty performance, where NMTLR had the lowest uncertainty(1.96%)and CSF had the highest uncertainty (14.29%). In conclusion, our method can estimate the uncertainty of individual patient survival modeling results. As expected, our data empirically demonstrate that as model uncertainty measured via our technique increases, the similarity between a feature-space and its predicted outcome decreases.
PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens

In the current state of 6D pose estimation, top-performing techniques depend on complex intermediate correspondences, specialized architectures, and non-end-to-end algorithms. In contrast, our research reframes the problem as a straightforward regression task by exploring the capabilities of Vision Transformers for direct 6D pose estimation through a tailored use of classification tokens. We also introduce a simple method for determining pose confidence, which can be readily integrated into most 6D pose estimation frameworks. This involves modifying the transformer architecture by decreasing the number of query elements based on the network's assessment of the scene complexity. Our method that we call Pose Vision Transformer or PViT-6D provides the benefits of simple implementation and being end-to-end learnable while outperforming current state-of-the-art methods by +0.3% ADD(-S) on Linemod-Occlusion and +2.7% ADD(-S) on the YCB-V dataset. Moreover, our method enhances both the model's interpretability and the reliability of its performance during inference.
Mirror Duality in Convex Optimization

While first-order optimization methods are usually designed to efficiently reduce the function value $f(x)$, there has been recent interest in methods efficiently reducing the magnitude of $\nabla f(x)$, and the findings show that the two types of methods exhibit a certain symmetry. In this work, we present mirror duality, a one-to-one correspondence between mirror-descent-type methods reducing function value and reducing gradient magnitude. Using mirror duality, we obtain the dual accelerated mirror descent (dual-AMD) method that efficiently reduces $\psi^*(\nabla f(x))$, where $\psi$ is a distance-generating function and $\psi^*$ quantifies the magnitude of $\nabla f(x)$. We then apply dual-AMD to efficiently reduce $\|\nabla f(\cdot) \|_q$ for $q\in [2,\infty)$ and to efficiently compute $\varepsilon$-approximate solutions of the optimal transport problem.
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning

Face recognition technology is widely used in the financial field, and various types of liveness attack behaviors need to be addressed. Existing liveness detection algorithms are trained on specific training datasets and tested on testing datasets, but their performance and robustness in transferring to unseen datasets are relatively poor. To tackle this issue, we propose a face liveness detection method based on image-text pairs and contrastive learning, dividing liveness attack problems in the financial field into eight categories and using text information to describe the images of these eight types of attacks. The text encoder and image encoder are used to extract feature vector representations for the classification description text and face images, respectively. By maximizing the similarity of positive samples and minimizing the similarity of negative samples, the model learns shared representations between images and texts. The proposed method is capable of effectively detecting specific liveness attack behaviors in certain scenarios, such as those occurring in dark environments or involving the tampering of ID card photos. Additionally, it is also effective in detecting traditional liveness attack methods, such as printing photo attacks and screen remake attacks. The zero-shot capabilities of face liveness detection on five public datasets, including NUAA, CASIA-FASD, Replay-Attack, OULU-NPU and MSU-MFSD also reaches the level of commercial algorithms. The detection capability of proposed algorithm was verified on 5 types of testing datasets, and the results show that the method outperformed commercial algorithms, and the detection rates reached 100% on multiple datasets. Demonstrating the effectiveness and robustness of introducing image-text pairs and contrastive learning into liveness detection tasks as proposed in this paper.
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement

Scene flow estimation, which aims to predict per-point 3D displacements of dynamic scenes, is a fundamental task in the computer vision field. However, previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges, and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems, we propose a novel uncertainty-aware scene flow estimation network (DifFlow3D) with the diffusion probabilistic model. Iterative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases, e.g. dynamics, noisy inputs, repetitive patterns, etc. To restrain the generation diversity, three key flow-related features are leveraged as conditions in our diffusion model. Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow. Our DifFlow3D achieves state-of-the-art performance, with 24.0% and 29.1% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably, our method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D) on the KITTI dataset. Additionally, our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into existing scene flow networks, significantly increasing their estimation accuracy. Codes are released at https://github.com/IRMVLab/DifFlow3D.
A low-cost Framework for Decentralized Autonomous Intersection Management

This paper addresses the traffic management problem for autonomous vehicles at intersections without traffic signals. In the current system, a road junction has no traffic signals when the traffic volume is low to medium. Installing infrastructure at each unsignalled crossing to coordinate autonomous cars can be formidable. We propose a novel low-cost solution strategy where the vehicles use a harmony matrix to find the best possible combination of the cars to cross the intersection without any crashes. The harmony matrix defines the connection between different vehicle maneuvers and is queried online for intersection management. We maximize the throughput of the intersection by solving a maximal clique problem formulated based on the vehicles present at the intersection. The proposed algorithm relies on the intent perceived by the autonomous vehicles. We compare our work with a communication-based strategy that uses V2I communication protocols, and through extensive simulation, we showed that our algorithm is comparable when the traffic volume is less than 500 PCUs/hr/lane.
End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames

Recently, temporal action detection (TAD) has seen significant performance improvement with end-to-end training. However, due to the memory bottleneck, only models with limited scales and limited data volumes can afford end-to-end training, which inevitably restricts TAD performance. In this paper, we reduce the memory consumption for end-to-end training, and manage to scale up the TAD backbone to 1 billion parameters and the input video to 1,536 frames, leading to significant detection performance. The key to our approach lies in our proposed temporal-informative adapter (TIA), which is a novel lightweight module that reduces training memory. Using TIA, we free the humongous backbone from learning to adapt to the TAD task by only updating the parameters in TIA. TIA also leads to better TAD representation by temporally aggregating context from adjacent frames throughout the backbone. We evaluate our model across four representative datasets. Owing to our efficient design, we are able to train end-to-end on VideoMAEv2-giant and achieve 75.4% mAP on THUMOS14, being the first end-to-end model to outperform the best feature-based methods.