Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex-Specific Variances in Anatomy and Blood Flow of the Left Main Coronary Bifurcation: Implications for Coronary Artery Disease Risk

Studies have shown marked sex disparities in Coronary Artery Diseases (CAD) epidemiology, yet the underlying mechanisms remain unclear. We explored sex disparities in the coronary anatomy and the resulting haemodynamics in patients with suspected, but no significant CAD. Left Main (LM) bifurcations were reconstructed from CTCA images of 127 cases (42 males and 85 females, aged 38 to 81). Detailed shape parameters were measured for comparison, including bifurcation angles, curvature, and diameters, before solving the haemodynamic metrics using CFD. The severity and location of the normalised vascular area exposed to physiologically adverse haemodynamics were statistically compared between sexes for all branches. We found significant differences between sexes in potentially adverse haemodynamics. Females were more likely than males to exhibit adversely low Time Averaged Endothelial Shear Stress along the inner wall of a bifurcation (16.8% vs. 10.7%). Males had a higher percentage of areas exposed to both adversely high Relative Residence Time (6.1% vs 4.2%, p=0.001) and high Oscillatory Shear Index (4.6% vs 2.3%, p<0.001). However, the OSI values were generally small and should be interpreted cautiously. Males had larger arteries (M vs F, LM: 4.0mm vs 3.3mm, LAD: 3.6mm 3.0mm, LCX:3.5mm vs 2.9mm), and females exhibited higher curvatures in all three branches (M vs F, LM: 0.40 vs 0.46, LAD: 0.45 vs 0.51, LCx: 0.47 vs 0.55, p<0.001) and larger inflow angle of the LM trunk (M: 12.9{\deg} vs F: 18.5{\deg}, p=0.025). Haemodynamic differences were found between male and female patients, which may contribute, at least in part, to differences in CAD risk. This work may facilitate a better understanding of sex differences in the clinical presentation of CAD, contributing to improved sex-specific screening, especially relevant for women with CAD who currently have worse predictive outcomes.
Three‐dimensional sequence analysis of a subsurface carbonate ramp, Mississippian Appalachian foreland basin, West Virginia, USA

Well‐cuttings, wireline logs and limited core and outcrop data were used to generate a regional, three‐dimensional sequence framework for Upper Mississippian (Chesterian), Greenbrier Group carbonates in the Appalachian foreland basin, West Virginia, USA. The resulting maps were used to document the stratigraphic response of the basin to tectonics and to glacio‐eustasy during the transition into ice‐house conditions. The ramp facies include inner ramp red beds and aeolianites, lagoonal muddy carbonates, mid‐ramp ooid and skeletal grainstone shoal complexes, and outer ramp wackestone–mudstone, that grades downslope into laminated silty lime mudstone. The facies make up fourth‐order sequences, a few metres to over 90 m (300 ft) thick. The sequences are bounded along the ramp margin by lowstand sandstones and calcareous siltstones. On the ramp, sequence boundaries are overlain by thin transgressive siliciclastics and aeolianites, and only a few are calichified. Maximum flooding surfaces on the outer ramp lie beneath deeper water facies that overlie lowstand to transgressive siliciclastic or carbonate units. On the shallow ramp, maximum flooding surfaces overlie siliciclastic‐prone transgressive systems tracts, that are overlain by highstand carbonates with significant grainstone units interlayered with lagoonal lime mudstones. The fourth‐order sequences are the major mappable subsurface units; they are bundled into weak composite sequences which are bounded by red beds. In spite of differential subsidence rates across the foreland basin (1 to 3 cm/k.y. up to 25 cm/k.y.), eustatic sea‐level changes controlled regional sequence development. Thrust‐load induced differential subsidence of fault‐blocks, coupled with in‐plane stress, controlled the rapid basinward thickening of the depositional wedge, whose thickness and facies were influenced by subtle structures such as arches trending at high angles as well as parallel to the margin.
Bulk-edge correspondence recovered in incompressible continuous media

Bulk-edge correspondence is a cornerstone in topological physics, establishing a connection between the number of unidirectional edge modes and the topological invariants that describe bulk wave singularities. In continuous media, this correspondence can be violated, resulting in weak topological protection of chiral edge states. Here, we propose a strategy to reestablish strong bulk-edge correspondence in incompressible continuous media. The key ideas involve handling the divergence-free constraint through an auxiliary field and considering a physical mechanism that imposes a high-frequency cutoff of bulk waves. When applied to rotating stratified fluids, this approach highlights the resilience of coastal Kelvin waves near vertical walls under varying boundary conditions.
Double-Virtual NNLO QCD Corrections for Five-Parton Scattering: The Quark Channels

We complete the computation of two-loop helicity amplitudes required to obtain next-to-next-to-leading order QCD corrections for three-jet production at hadron colliders, including all contributions beyond the leading-color approximation. The analytic expressions are reconstructed from finite-field samples obtained with the numerical unitarity method. We find that the reconstruction is significantly facilitated by exploiting the overlaps between rational coefficient functions of quark and gluon processes, and we display their compact generating sets in the appendix of the paper. We implement our results in a public code, and demonstrate its suitability for phenomenological applications.
MLLMs-Augmented Visual-Language Representation Learning

Visual-language pre-training has achieved remarkable success in many multi-modal tasks, largely attributed to the availability of large-scale image-text datasets. In this work, we demonstrate that Multi-modal Large Language Models (MLLMs) can enhance visual-language representation learning by establishing richer image-text associations for image-text datasets. Our approach is simple, utilizing MLLMs to extend multiple diverse captions for each image. To prevent the bias introduced by MLLMs' hallucinations and monotonous language styles, we propose"text shearing"to maintain the quality and availability of extended captions. In image-text retrieval, without introducing additional training cost, our method consistently obtains 5.6 ~ 35.0 and 16.8 ~ 46.1 improvement on Recall@1 under the fine-tuning and zero-shot settings, respectively. Notably, we obtain zero-shot results that are comparable to fine-tuning on target datasets, which encourages more exploration of the versatile use of MLLMs.
A causal modelling analysis of Bell scenarios in space-time: implications of jamming non-local correlations for relativistic causality principles

Bell scenarios involve space-like separated measurements made by multiple parties. The standard no-signalling constraints ensure that such parties cannot signal superluminally by choosing their measurement settings. In tripartite Bell scenarios, relaxed non-signalling constraints have been proposed, which permit a class of post-quantum theories known as jamming non-local theories. To analyse whether no superluminal signalling continues to hold in these theories and, more generally, the role of non-signalling constraints in preserving relativistic causality principles, we apply a framework that we have recently developed for defining information-theoretic causal models in non-classical theories and their compatibility with relativistic causality in a space-time. We show that any theory that generates jamming correlations in a Bell scenario between space-like separated parties must necessarily do so through causal fine-tuning and by means of superluminal causal influences. Moreover, within our framework, we show that jamming theories can also lead to superluminal signalling (contrary to previous claims) unless it is ensured that certain systems are fundamentally inaccessible to agents and their interventions. Finally, we analyse relativistic causality in Bell scenarios showing that no-signalling constraints on correlations are generally insufficient for ruling out superluminal signalling when general interventions are also allowed. In this way, we identify necessary and sufficient conditions for ruling out superluminal signalling in Bell scenarios, and demonstrate through examples that the non-signalling constraints on correlations are neither necessary nor sufficient for ruling out causal loops. These results solidify our understanding of relativistic causality principles in information processing tasks in space-time, involving classical, quantum or post-quantum resources.
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans

The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
Does ESG and Digital Transformation affects Corporate Sustainability? The Moderating role of Green Innovation

Recently, environmental, social, and governance (ESG) has become an important factor in companies' sustainable development. Artificial intelligence (AI) is also a core digital technology that can create innovative, sustainable, comprehensive, and resilient environments. ESG- and AI-based digital transformation is a relevant strategy for managing business value and sustainability in corporate green management operations. Therefore, this study examines how corporate sustainability relates to ESG- and AI-based digital transformation. Furthermore, it confirms the moderating effect of green innovation on the process of increasing sustainability. To achieve the purpose of this study, 359 data points collected for hypothesis testing were used for statistical analysis and for mobile business platform users. The following conclusions are drawn. (1) ESG activities have become key variables that enable sustainable corporate growth. Companies can implement eco-friendly operating processes through ESG activities. (2) This study verifies the relationship between AI-based digital transformation and corporate sustainability and confirms that digital transformation positively affects corporate sustainability. In addition, societal problems can be identified and environmental accidents prevented through technological innovation. (3) This study does not verify the positive moderating effect of green innovation; however, it emphasizes its necessity and importance. Although green innovation improves performance only in the long term, it is a key factor for companies pursuing sustainable growth. This study reveals that ESG- and AI-based digital transformation is an important tool for promoting corporate sustainability, broadening the literature in related fields and providing insights for corporate management and government policymakers to advance corporate sustainability.
Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis

This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion (LRDiff) framework. Vision Guidance, a spatial layout condition, acts as a clue in the perturbed distribution, greatly narrowing down the search space, to focus on the image sampling process adhering to the spatial layout condition. The LRDiff framework constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object. Such a layered rendering strategy effectively prevents issues like unintended conceptual blending or mismatches, while allowing for more coherent and contextually accurate image synthesis. The proposed method provides a more efficient and accurate means of synthesising images that align with specific spatial and contextual requirements. We demonstrate through our experiments that our method provides better results than existing techniques both quantitatively and qualitatively. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.
Semiparametric Efficient Inference in Adaptive Experiments

We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit theorem for the Adaptive Augmented Inverse-Probability Weighted estimator, which is semiparametric efficient, under weaker assumptions than those previously made in the literature. This central limit theorem enables efficient inference at fixed sample sizes. We then consider a sequential inference setting, deriving both asymptotic and nonasymptotic confidence sequences that are considerably tighter than previous methods. These anytime-valid methods enable inference under data-dependent stopping times (sample sizes). Additionally, we use propensity score truncation techniques from the recent off-policy estimation literature to reduce the finite sample variance of our estimator without affecting the asymptotic variance. Empirical results demonstrate that our methods yield narrower confidence sequences than those previously developed in the literature while maintaining time-uniform error control.
Local Geometry Determines Global Landscape in Low-rank Factorization for Synchronization

The orthogonal group synchronization problem, which focuses on recovering orthogonal group elements from their corrupted pairwise measurements, encompasses examples such as high-dimensional Kuramoto model on general signed networks, $\mathbb{Z}_2$-synchronization, community detection under stochastic block models, and orthogonal Procrustes problem. The semidefinite relaxation (SDR) has proven its power in solving this problem; however, its expensive computational costs impede its widespread practical applications. We consider the Burer-Monteiro factorization approach to the orthogonal group synchronization, an effective and scalable low-rank factorization to solve large scale SDPs. Despite the significant empirical successes of this factorization approach, it is still a challenging task to understand when the nonconvex optimization landscape is benign, i.e., the optimization landscape possesses only one local minimizer, which is also global. In this work, we demonstrate that if the degree of freedom within the factorization exceeds twice the condition number of the ``Laplacian"(certificate matrix) at the global minimizer, the optimization landscape is absent of spurious local minima. Our main theorem is purely algebraic and versatile, and it seamlessly applies to all the aforementioned examples: the nonconvex landscape remains benign under almost identical condition that enables the success of the SDR. Additionally, we illustrate that the Burer-Monteiro factorization is robust to ``monotone adversaries", mirroring the resilience of the SDR. In other words, introducing ``favorable"adversaries into the data will not result in the emergence of new spurious local minimizers.
Direct determination of the fully differential cross section by the time-dependent wave function

This paper focused on showing that the fully differential cross section of ionization during a collision of a proton and an antiproton with a hydrogen atom is directly expressed by the time-dependent wave function. For the projectile, wave function corresponding to the specific scattering angle was converted by two-dimensional Fourier transform from the wave functions of corresponding to impact parameters. This wave function shows how the ejected electron probability density distribution varies with time. We are shown that the calculation of the fully differential cross section of ionization can be directly determined by the local value of the wave function without the need to calculate the spatial integral for calculating the transition amplitude. It has been shown that the direct determination of the fully differential cross section by this time-dependent wave function is in good agreement with the results of determined the traditional method is by the transition amplitude.
Reduced Set Theory

We present a new fragment of axiomatic set theory for pure sets and for the iteration of power sets within given transitive sets. It turns out that this formal system admits an interesting hierarchy of models with true membership relation and with only finite or countably infinite ordinals. Still a considerable part of mathematics can be formalized within this system.
SMaRt: Improving GANs with Score Matching Regularity

Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex. In this work, we revisit the mathematical foundations of GANs, and theoretically reveal that the native adversarial loss for GAN training is insufficient to fix the problem of subsets with positive Lebesgue measure of the generated data manifold lying out of the real data manifold. Instead, we find that score matching serves as a promising solution to this issue thanks to its capability of persistently pushing the generated data points towards the real data manifold. We thereby propose to improve the optimization of GANs with score matching regularity (SMaRt). Regarding the empirical evidences, we first design a toy example to show that training GANs by the aid of a ground-truth score function can help reproduce the real data distribution more accurately, and then confirm that our approach can consistently boost the synthesis performance of various state-of-the-art GANs on real-world datasets with pre-trained diffusion models acting as the approximate score function. For instance, when training Aurora on the ImageNet 64x64 dataset, we manage to improve FID from 8.87 to 7.11, on par with the performance of one-step consistency model. The source code will be made public.
Monsoon Breaks and Subseasonal Sea Surface Temperature Variability in the Bay of Bengal *

The Indian southwest monsoon directly affects the lives of over one billion people, providing almost 90% of the annual precipitation to the Indian subcontinent. An important characteristic of the southwest monsoon is variability on subseasonal timescales, with ‘‘active’’ periods of heavy rain interrupted by drier ‘‘break’’ periods. Both the number of monsoon breaks in a season and the timing of these breaks profoundly impact agricultural output from the Indian subcontinent. Most research on monsoon breaks has emphasized possible atmospheric mechanisms. However, new satellite data reveal large-amplitude basin-scale subseasonal sea surface temperature (SST) variability in the Bay of Bengal (BoB), in which northern BoB cooling precedes monsoon breaks by about 1 week. The relationship is statistically signiﬁcant at the 95% level over the 3 yr examined, and so offers a potential statistical predictor for short-term monsoon variability. The basinwide averaged amplitude of SST changes is 1 (cid:56) –2 (cid:56) C and local changes can exceed 3 (cid:56) C over 2 weeks; these changes are as large as those seen in the local climatological seasonal cycle. This raises the possibility that air–sea interaction may be a signiﬁcant factor in monsoon variability; the SST variability is coherent with monsoon variability with a phase relationship consistent with a coupled oscillation. A schematic coupled air–sea oscillator mechanism is offered for further study, in which oceanic changes play a dynamical role in monsoon variability.
Tobelitization of smectite during oil generation in oil-source shales. Application to North Sea Illite-tobelite-smectite-vermiculite

Illite-smectite (I-S) minerals isolated from Upper Jurassic oil-source rock shales from Denmark and the North Sea have been investigated by X-ray diffraction, thermal analysis, infrared, Mössbauer, and solid-state nuclear magnetic resonance spectroscopies and chemical analysis. Detailed structures have been determined in order to reveal the diagenetic transformation mechanism in these shales. Generally, in oil-source rocks of sedimentary basins, oil generation takes place simultaneously with the diagenetic transformation of I-S. We demonstrate a link between the two reactions: NH3 released from kerogen during maximum oil generation is fixed as NH4+ in the NH4-bearing mica or tobelite layers formed from smectite or vermiculite layers in I-S, in a diagenetic interval which we name the ‘tobelitization window’. Due to this solid-state transformation, mixed-layer structures have been formed consisting of interstratified illite, tobelite, smectite and vermiculite layers (I-T-S-V) and having maximum ordering of illite + tobelite and smectite layers for R = 1. The tobelitization of smectite in I-S is probably typical for all oil-source rock shales.
Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.