Miracle-dz/UniFAR-FTUs huggingface dataset:
Large‐magnitude Oligo‐Miocene extension in southern Sonora: Implications for the tectonic evolution of northwest Mexico

New structural and 40Ar/39Ar data from the Santa Rosa area, southeastern Sonora shed light on the magnitude and timing of extensional tectonism. Episodic magmatic activity from the Late Cretaceous to the Miocene is recorded by a 3–4 km thick structural/stratigraphic section that includes a composite Laramide granodiorite batholith and its andesitic wall rocks, early to middle Tertiary rhyolite ignimbrites and silicic domes, and late Oligocene to middle Miocene mafic lavas interbedded with tuffaceous sediment and fanglomerate. The older units are complexly faulted and steeply tilted by several generations of normal faults. Palinspastic reconstructions indicate cumulative extension of ∼90%. Early gently dipping, NW striking normal faults document major extension oriented −N50°E. Angular unconformities and growth fault relations within Oligo‐Miocene sequences bracket this extension between 26 and ∼20 Ma. Younger, widely spaced NS to NNW trending, high‐angle normal faults cut the previously faulted and tilted sections and produced modest (10–15%) E‐W extension that is largely bracketed between 20 and 17 Ma. The magnitude of Neogene extension in this region is greater and the timing is older than previously recognized. Reconnaissance of other areas between the Sierra Madre Occidental and the coast at this latitude suggests that most extension in Sonora occurred between ∼27 and 12 Ma, while remnants of the Farallon plate were still being subducted. Plate‐tectonic models that predict substantial extension in Sonora during proto‐Gulf (∼10 – 5 Ma) transtensional deformation may need to be reevaluated. An alternative model, presented here, is that Baja California began moving with Pacific plate motion shortly after the ∼11 Ma termination of subduction and that northwest motion of Baja relative to mainland Mexico might total 500 km, distributed across the previously extended Oligo‐Miocene magmatic arc.
LAC - Latent Action Composition for Skeleton-based Action Segmentation

Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features from skeleton sequences and then processing them by a temporal model to classify frame-wise actions. However, their performances remain limited as the visual features cannot sufficiently express composable actions. In this context, we propose Latent Action Composition (LAC)1, a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation. LAC is composed of a novel generation module towards synthesizing new sequences. Specifically, we design a linear latent space in the generator to represent primitive motion. New composed motions can be synthe-sized by simply performing arithmetic operations on latent representations of multiple input skeleton sequences. LAC leverages such synthesized sequences, which have large diversity and complexity, for learning visual representations of skeletons in both sequence and frame spaces via contrastive learning. The resulting visual encoder has a high expressive power and can be effectively transferred onto action segmentation tasks by end-to-end fine-tuning without the need for additional temporal models. We conduct a study focusing on transfer-learning and we show that representations learned from pre-trained LAC outperform the state-of-the-art by a large margin on TSU, Charades, PKU-MMD datasets.
SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.
s-stability for W^{s,n/s}-harmonic maps in homotopy groups

We study $s$-dependence for minimizing $W^{s,n/s}$-harmonic maps $u\colon \mathbb{S}^n \to \mathbb{S}^\ell$ in homotopy classes. Sacks--Uhlenbeck theory shows that, for each $s$, minimizers exist in a generating subset of $\pi_{n}(\mathbb{S}^\ell)$. We show that this generating subset can be chosen locally constant in $s$. We also show that as $s$ varies the minimal $W^{s,n/s}$-energy in each homotopy class changes continuously. In particular, we provide progress to a question raised by Mironescu and Brezis--Mironescu.
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models

Code datasets are of immense value for training neural-network-based code completion models, where companies or organizations have made substantial investments to establish and process these datasets. Unluckily, these datasets, either built for proprietary or public usage, face the high risk of unauthorized exploits, resulting from data leakages, license violations, etc. Even worse, the "black-box" nature of neural models sets a high barrier for externals to audit their training datasets, which further connives these unauthorized usages. Currently, watermarking methods have been proposed to prohibit inappropriate usage of image and natural language datasets. However, due to domain specificity, they are not directly applicable to code datasets, leaving the copyright protection of this emerging and important field of code data still exposed to threats. To fill this gap, we propose a method, named CodeMark, to embed user-defined imperceptible watermarks into code datasets to trace their usage in training neural code completion models. CodeMark is based on adaptive semantic-preserving transformations, which preserve the exact functionality of the code data and keep the changes covert against rule-breakers. We implement CodeMark in a toolkit and conduct an extensive evaluation of code completion models. CodeMark is validated to fulfill all desired properties of practical watermarks, including harmlessness to model accuracy, verifiability, robustness, and imperceptibility.
Residual Denoising Diffusion Models

We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion. This dual diffusion framework expands the denoising-based diffusion models, initially uninterpretable for image restoration, into a unified and interpretable model for both image generation and restoration by introducing residuals. Specifically, our residual diffusion represents directional diffusion from the target image to the degraded input image and explicitly guides the reverse generation process for image restoration, while noise diffusion represents random perturbations in the diffusion process. The residual prioritizes certainty, while the noise emphasizes diversity, enabling RDDM to effectively unify tasks with varying certainty or diversity requirements, such as image generation and restoration. We demonstrate that our sampling process is consistent with that of DDPM and DDIM through coefficient transformation, and propose a partially path-independent generation process to better understand the reverse process. Notably, our RDDM enables a generic UNet, trained with only an L1 loss and a batch size of 1, to compete with state-of-the-art image restoration methods. We provide code and pre-trained models to encourage further exploration, application, and development of our innovative framework (https://github.com/nachifur/RDDM).
Comparison of the long-term efficacy and safety of generic tacrolimus, Tacrobell, with Prograf in liver transplant recipients

Introduction: The purpose of this study was to compare the safety and efficacy of generic tacrolimus (Tacrobell [TCB]) and a reference tacrolimus (Prograf [PGF]) in liver transplant recipients. Patients and methods: We retrospectively analyzed 167 patients who used TCB or PGF between January 2009 and March 2016 for . 1 year (TCB group, n = 86; PGF group, n = 81). To assess the efficacy and safety of TCB, we evaluated the relationship between drug dose and trough level, survival, rejection, infection, kidney function, and side effects. Results: There was no difference in the preoperative demographics between the two groups. Moreover, there was no difference in the drug dose and trough level between the groups at 1 week after surgery. Coefficient of variation (CV) values were obtained at the drug trough level for each patient and no differences in CV values were identified within 1 year ( p = 0.587) and up to 5 years ( p = 0.824) in both groups. Rehospitalization ( p = 0.1) and total rejection ( p = 0.915) did not differ between the two groups, but the rejection severity, recorded as the rejection activity index value, was worse in the PGF group ( p = 0.039). No difference was found in the infection rate ( p = 0.818), and with regard to nephrotoxicity, there was no difference in the rate of patients with chronic kidney disease stage 3 and above during the follow-up period. No differences were found between the two groups in terms of drug side effects and adverse events. Conclusion: The generic tacrolimus, TCB, is a comparable alternative to the original tacrolimus, PGF, as a main immunosuppressive drug for liver transplantation.
On the Depth between Beam Search and Exhaustive Search for Text Generation

Beam search and exhaustive search are two extreme ends of text decoding algorithms with respect to the search depth. Beam search is limited in both search width and depth, whereas exhaustive search is a global search that has no such limitations. Surprisingly, beam search is not only computationally cheaper but also performs better than exhaustive search despite its higher search error. Plenty of research has investigated a range of beam widths, from small to large, and reported that a beam width that is neither too large nor too small is desirable. However, in terms of search depth, only the two extreme ends, beam search and exhaustive search are studied intensively. In this paper, we examine a range of search depths between the two extremes to discover the desirable search depth. To this end, we introduce Lookahead Beam Search (LBS), a multi-step lookahead search that optimizes the objective considering a fixed number of future steps. Beam search and exhaustive search are special cases of LBS where the lookahead depth is set to $0$ and $\infty$, respectively. We empirically evaluate the performance of LBS and find that it outperforms beam search overall on machine translation tasks. The result suggests there is room for improvement in beam search by searching deeper. Inspired by the analysis, we propose Lookbehind Heuristic Beam Search, a computationally feasible search algorithm that heuristically simulates LBS with 1-step lookahead. The empirical results show that the proposed method outperforms vanilla beam search on machine translation and text summarization tasks.
Spreading of a viscoelastic drop on a solid substrate

We study the spreading of viscous and viscoelastic drops on solid substrates with different wettability. In the early stages of spreading, we find that the viscoelastic drop spreads with faster and a different power law than the Newtonian drop (i.e. aqueous glycerine solution) for the same zero shear rate viscosity. We argue that the effect of viscoelasticity is only observable for experimental time scales in the order of the internal relaxation time of the polymer solution or longer times. Near the contact line, the effective viscosity is lower for the viscoelastic drop than for the Newtonian drop. Together with its shear rate dependency, this difference in effective viscosity can explain the different spreading dynamics. We support our experimental findings with a simple perturbation model that qualitatively agrees with our findings.
SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding

Temporal Interaction Graphs (TIGs) are widely employed to model intricate real-world systems such as financial systems and social networks. To capture the dynamism and interdependencies of nodes, existing TIG embedding models need to process edges sequentially and chronologically. However, this requirement prevents it from being processed in parallel and struggle to accommodate burgeoning data volumes to GPU. Consequently, many large-scale temporal interaction graphs are confined to CPU processing. Furthermore, a generalized GPU scaling and acceleration approach remains unavailable. To facilitate large-scale TIGs' implementation on GPUs for acceleration, we introduce a novel training approach namely Streaming Edge Partitioning and Parallel Acceleration for Temporal Interaction Graph Embedding (SPEED). The SPEED is comprised of a Streaming Edge Partitioning Component (SEP) which addresses space overhead issue by assigning fewer nodes to each GPU, and a Parallel Acceleration Component (PAC) which enables simultaneous training of different sub-graphs, addressing time overhead issue. Our method can achieve a good balance in computing resources, computing time, and downstream task performance. Empirical validation across 7 real-world datasets demonstrates the potential to expedite training speeds by a factor of up to 19.29x. Simultaneously, resource consumption of a single-GPU can be diminished by up to 69%, thus enabling the multiple GPU-based training and acceleration encompassing millions of nodes and billions of edges. Furthermore, our approach also maintains its competitiveness in downstream tasks.
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN). However, the features from CT scans alone are not always sufficient for the diagnosis of PE. CT scans along with electronic heath records (EHR) can provide a better insight into the patients condition and can lead to more accurate PE diagnosis. In this paper, we propose Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised contrastive pretraining strategy that employs both the patients CT scans as well as the EHR data, aiming to enhance the alignment of feature representations between the two modalities and leverage information to improve the PE diagnosis. In order to achieve this, we make use of the class labels and pull the sample features of the same class together, while pushing away those of the other class. Results show that the proposed work outperforms the existing techniques and achieves state-of-the-art performance on the RadFusion dataset with an F1-score of 0.913, accuracy of 0.90 and an AUROC of 0.943. Furthermore, we also explore the explainability of our approach in comparison to other methods. Our code is publicly available at https://github.com/BioMedIA-MBZUAI/PECon.
Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: II. Dynamics forecasting

In part I of the article, we demonstrated that a variant of the dynamic mode decomposition (DMD) algorithm based on variable projection optimization, called optimized DMD (OPT-DMD), enables a robust identification of the dominant spatiotemporally coherent modes underlying the data across various test cases representing different physical parameters in an E × B simulation configuration. We emphasized that the OPT-DMD significantly improves the analysis of complex plasma processes, revealing information that cannot be derived using conventionally employed analyses such as the fast Fourier transform. As the OPT-DMD can be constrained to produce stable reduced-order models (ROMs) by construction, in this paper, we extend the application of the OPT-DMD and investigate the capabilities of the linear ROM from this algorithm toward forecasting in time of the plasma dynamics in configurations representative of the radial-azimuthal and axial-azimuthal cross-sections of a Hall thruster and over a range of simulation parameters in each test case. The predictive capacity of the OPT-DMD ROM is assessed primarily in terms of short-term dynamics forecast or, in other words, for large ratios of training-to-test data. However, the utility of the ROM for long-term dynamics forecasting is also presented for an example case in the radial-azimuthal configuration. The model’s predictive performance is heterogeneous across various test cases. Nonetheless, a remarkable predictiveness is observed in the test cases that do not exhibit highly transient behaviors. Moreover, in all investigated cases, the error between the ground-truth and the reconstructed data from the OPT-DMD ROM remains bounded over time within both the training and the test window. As a result, despite its limitation in terms of generalized applicability to all plasma conditions, the OPT-DMD is proven as a reliable method to develop low computational cost and highly predictive data-driven ROMs in systems with a quasi-periodic global evolution of the plasma state.
Bromodomain and Extra-terminal (BET) Protein Inhibitors Suppress Chondrocyte Differentiation and Restrain Bone Growth*

Small molecule inhibitors for bromodomain and extra-terminal (BET) proteins have recently emerged as potential therapeutic agents in clinical trials for various cancers. However, to date, it is unknown whether these inhibitors have side effects on bone structures. Here, we report that inhibition of BET bromodomain proteins may suppress chondrocyte differentiation and restrain bone growth. We generated a luciferase reporter system using the chondrogenic cell line ATDC5 in which the luciferase gene was driven by the promoter of Col2a1, an elementary collagen of the chondrocyte. The Col2a1-luciferase ATDC5 system was used for rapidly screening both activators and repressors of human collagen Col2a1 gene expression, and we found that BET bromodomain inhibitors reduce the Col2a1-luciferase. Consistent with the luciferase assay, BET inhibitors decrease the expression of Col2a1. Furthermore, we constructed a zebrafish line in which the enhanced green fluorescent protein (EGFP) expression was driven by col2a1 promoter. The transgenic (col2a1-EGFP) zebrafish line demonstrated that BET inhibitors I-BET151 and (+)-JQ1 may affect EGFP expression in zebrafish. Furthermore, we found that I-BET151 and (+)-JQ1 may affect chondrocyte differentiation in vitro and inhibit zebrafish growth in vivo. Mechanistic analysis revealed that BET inhibitors influenced the depletion of RNA polymerase II from the Col2a1 promoter. Collectively, these results suggest that BET bromodomain inhibition may have side effects on skeletal bone structures.
Photon escape cones, physical and optical metrics, asymptotic and near-horizon physics

We consider the explicit analytic behaviour of photon escape cones in generic static spherically symmetric spacetimes, emphasizing the interplay between the physical spacetime metric and the optical metric, and the interplay between large-distance asymptotic and near-horizon physics. The circular photon orbits (photon spheres) are shown to be given by wormhole throats in the optical metric, (not the physical metric), and the escape cone solid angle is easily calculable in terms of the capture cross section, sigma_{capture}, the area of the spherical 2-surfaces, and the norm of the timelike Killing vector. Under appropriate circumstances, for near-horizon photon emission the escape cone solid angle can be related to the surface gravity kappa_H. We provide a number of illustrative examples, involving both black holes and wormholes, including situations with multiple photon spheres.
Magnetic Resonance Imaging‒Targeted Biopsy of the Prostate

Description of Procedure or Service Populations Interventions Comparators Outcomes Individuals: • With suspicion of prostate cancer Interventions of interest are: • Magnetic resonance imaging‒targeted biopsy Comparators of interest are: • Standard transrectal ultrasoundguided biopsy Relevant outcomes include: • Overall survival • Disease-specific survival • Test accuracy • Morbid events • Quality of life Individuals: • With prostate cancer and in active surveillance Interventions of interest are: • Magnetic resonance imaging‒targeted biopsy Comparators of interest are: • Standard transrectal ultrasoundguided biopsy Relevant outcomes include: • Overall survival • Disease-specific survival • Test accuracy • Morbid events • Quality of life
Modified special HSS method for discrete ill-posed problems and image restoration

ABSTRACT For solving ill-posed inverse problems, we establish a modified version of special Hermitian and skew-Hermitian splitting (SHSS) iteration method, called the modified SHSS (MSHSS) method in this paper. Based on an augmented system formulation, we apply the modified splitting to solve ill-posed inverse problems, and theoretically study its convergence and determine its optimal iteration parameters. Furthermore, the SOR acceleration scheme for MSHSS iterative method is discussed. Finally, numerical experiments arising from a Fredholm integral equation of the first kind and image restoration are used to further examine the effectiveness of the new method.