Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Time Domain Formulation for Broadband Noise Predictions

A new analytic result in acoustics called “Formulation 1B,” proposed by Farassat, is used to compute the loading noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Williams-Hawkings equation with the loading source term. The formulation contains a far field surface integral that depends on the time derivative and the surface gradient of the pressure on the airfoil, as well as a contour integral on the boundary of the airfoil surface. As a first test case, the new formulation is used to compute the noise radiated from a flat plate, moving through a sinusoidal gust of constant frequency. The unsteady surface pressure for this test case is analytically specied from a result based on linear airfoil theory. This test case is used to examine the velocity scaling properties of Formulation 1B and to demonstrate its equivalence to Formulation 1A of Farassat. The new acoustic formulation, again with an analytic surface pressure, is then used to predict broadband noise radiated from an airfoil immersed in homogeneous, isotropic turbulence. The results are compared with experimental data previously reported by Paterson and Amiet. Good agreement between predictions and measurements is obtained. Finally, an alternative form of Formulation 1B is described for statistical analysis of broadband noise.
Measurement-Induced Nonlocality for Observers Near a Black Hole

We present a systematic and complementary study of quantum correlations near a black hole by considering measurement-induced nonlocality (MIN). The quantum measure of interest is discussed for the fermionic, bosonic and mixed fermion–boson modes on equal footing with respect to the Hawking radiation. The obtained results show that in the infinite Hawking temperature limit, the physically accessible correlations do not vanish only in the fermionic case. However, the higher frequency modes can sustain correlations for the finite Hawking temperature, with mixed systems being more sensitive towards the increase in the fermionic frequencies than the bosonic ones. Since the MIN for the latter modes quickly diminishes, the increased frequency may be a way to maintain nonlocal correlations for the scenarios at the finite Hawking temperature.
Explainable Semantic Medical Image Segmentation with Style

Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack of high-quality semantically labelled data remains a challenge leading to model brittleness to small shifts to input data. Most works require extra data for semi-supervised learning and lack the interpretability of the boundaries of the training data distribution during training, which is essential for model deployment in clinical practice. We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data by simultaneously constructing an explorable manifold during training. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation. All the while, the discriminator guides the manifold learning by supervising the semantic content and fine-grained features separately during the image diversification. After training, visualisation of the learnt manifold from the generator is available to interpret the model limits. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods while being more explainable using an explorable manifold.
A pseudo-likelihood approach to community detection in weighted networks

Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns. While many community detection methods have been developed for networks with binary edges, few of them are applicable to networks with weighted edges, which are common in practice. We propose a pseudo-likelihood community estimation algorithm derived under the weighted stochastic block model for networks with normally distributed edge weights, extending the pseudo-likelihood algorithm for binary networks, which offers some of the best combinations of accuracy and computational efficiency. We prove that the estimates obtained by the proposed method are consistent under the assumption of homogeneous networks, a weighted analogue of the planted partition model, and show that they work well in practice for both homogeneous and heterogeneous networks. We illustrate the method on simulated networks and on a fMRI dataset, where edge weights represent connectivity between brain regions and are expected to be close to normal in distribution by construction.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.
The Role of Natal Kicks in Forming Asymmetric Compact Binary Mergers

In their most recent observing run, the LIGO-Virgo-KAGRA Collaboration observed gravitational waves from compact binary mergers with highly asymmetric mass ratios, including both binary black holes (BBHs) and neutron star-black holes (NSBHs). It appears that NSBHs with mass ratios q ≃ 0.2 are more common than equally asymmetric BBHs, but the reason for this remains unclear. We use the binary population synthesis code cosmic to investigate the evolutionary pathways leading to the formation and merger of asymmetric compact binaries. We find that within the context of isolated binary stellar evolution, most asymmetric mergers start off as asymmetric stellar binaries. Because of the initial asymmetry, these systems tend to first undergo a dynamically unstable mass transfer phase. However, after the first star collapses into a compact object, the mass ratio is close to unity and the second phase of mass transfer is usually stable. According to our simulations, this stable mass transfer fails to shrink the orbit enough on its own for the system to merge. Instead, the natal kick received by the second-born compact object during its collapse is key in determining how many of these systems can merge. For the most asymmetric systems with mass ratios of q ≤ 0.1, the merging systems in our models receive an average kick magnitude of 255 km s−1 during the second collapse, while the average kick for non-merging systems is 59 km s−1. Because lower mass compact objects, like neutron stars, are expected to receive larger natal kicks than higher mass BHs, this may explain why asymmetric NSBH systems merge more frequently than asymmetric BBH systems.
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection

Few-shot object detection (FSOD) is a challenging problem aimed at detecting novel concepts from few exemplars. Existing approaches to FSOD all assume abundant base labels to adapt to novel objects. This paper studies the new task of semi-supervised FSOD by considering a realistic scenario in which both base and novel labels are simultaneously scarce. We explore the utility of unlabeled data within our proposed label-efficient detection framework and discover its remarkable ability to boost semi-supervised FSOD by way of region proposals. Motivated by this finding, we introduce SoftER Teacher, a robust detector combining pseudo-labeling with consistency learning on region proposals, to harness unlabeled data for improved FSOD without relying on abundant labels. Rigorous experiments show that SoftER Teacher surpasses the novel performance of a strong supervised detector using only 10% of required base labels, without catastrophic forgetting observed in prior approaches. Our work also sheds light on a potential relationship between semi-supervised and few-shot detection suggesting that a stronger semi-supervised detector leads to a more effective few-shot detector.
Optimal foraging strategies can be learned

The foraging behavior of animals is a paradigm of target search in nature. Understanding which foraging strategies are optimal and how animals learn them are central challenges in modeling animal foraging. While the question of optimality has wide-ranging implications across fields such as economy, physics, and ecology, the question of learnability is a topic of ongoing debate in evolutionary biology. Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning (RL) framework. To this end, we model foragers as learning agents. We first prove theoretically that maximizing rewards in our RL model is equivalent to optimizing foraging efficiency. We then show with numerical experiments that, in the paradigmatic model of non-destructive search, our agents learn foraging strategies which outperform the efficiency of some of the best known strategies such as Lévy walks. These findings highlight the potential of RL as a versatile framework not only for optimizing search strategies but also to model the learning process, thus shedding light on the role of learning in natural optimization processes.
Finite Element Approximation of Data-Driven Problems in Conductivity

This paper is concerned with the finite element discretization of the data driven approach according to arXiv:1510.04232 for the solution of PDEs with a material law arising from measurement data. To simplify the setting, we focus on a scalar diffusion problem instead of a problem in elasticity. It is proven that the data convergence analysis from arXiv:1708.02880 carries over to the finite element discretization as long as $H(\mathrm{div})$-conforming finite elements such as the Raviart-Thomas element are used. As a corollary, minimizers of the discretized problems converge in data in the sense of arXiv:1708.02880, as the mesh size tends to zero and the approximation of the local material data set gets more and more accurate. We moreover present several heuristics for the solution of the discretized data driven problems, which is equivalent to a quadratic semi-assignment problem and therefore NP-hard. We test these heuristics by means of two examples and it turns out that the"classical"alternating projection method according to arXiv:1510.04232 is superior w.r. t. the ratio of accuracy and computational time.
A Coupled Ocean–Atmosphere Instability of Relevance to the Seasonal Cycle

Abstract Recent observational studies have suggested that interactions between the atmosphere and the ocean play an important role in the pronounced annual cycle of the eastern equatorial Pacific and Atlantic Oceans. The key to this atmosphere–ocean interaction is a positive feedback between the surface winds and the local SST gradients in the cold tongue/ITCZ complex regions, which leads to an instability in the coupled system. By means of linear instability analyses and numerical model experiments, such an instability mechanism is explored in a simple coupled ocean-atmosphere system. The instability analysis yields a family of antisymmetric and symmetric unstable SST modes. The antisymmetric mode has the most rapid growth rate. The most unstable antisymmetric mode occurs at zero wavenumber and has zero frequency. The symmetric SST mode, although its growth rate is smaller, has a structure at annual period that appears to resemble the observed westward propagating feature in the annual cycle of near-equa.
International comparison of /sup 127/I/sub 2/-stabilized He-Ne lasers at /spl lambda/=633 nm using the third and/or the fifth harmonic locking technique

An international comparison has been carried out of /sup 127/I/sub 2/-stabilized helium-neon lasers at /spl lambda/=633 nm from the CMI and the BIPM using the beat frequency technique. The lasers were stabilized by the third (THT) and/or the fifth (FHT), harmonic locking technique. Using the FHT, the frequency of lasers is shifted to higher frequencies. An iodine cell designated JM-03 was used to evaluate the frequency shifts produced by weaker components of the rotational-vibrational transition P(33) 6-3 in the mixed molecule /sup 127/I/sup 129/I. Laser comparison shows that the frequencies of components d, e, f, g of the R(127) 11-5 of the molecule /sup 127/I/sub 2/ are very strongly influenced by the weaker spectrum of components of the mixed molecule. <<ETX>>
International Vaccine Allocation: An Optimization Framework (preprint)

The global SARS-CoV-2 (COVID-19) pandemic highlighted the challenge of equitable vaccine distribution between high- and low-income countries. High-income countries, such as the United States, were among the first to acquire the rapidly developed vaccines against COVID-19. However, many such high-income countries were reluctant or slow to distribute extra doses of the vaccine to lower-income countries via the COVID-19 Vaccines Global Access (COVAX) collaboration [Clinton and Yoo 2022]. In addition to moral objections to such vaccine nationalism, vaccine inequity during a pandemic could contribute to the evolution of new variants of the virus and possibly increase total deaths, including in the high-income countries. This paper uses the COVID-19 pandemic as a case study to identify scenarios under which it might be in a high-income nation's own interest to donate vaccine doses to another country before its own population has been fully vaccinated. Using an epidemiological model embedded in an optimization framework, we identify realistic scenarios under which a donor country prefers to donate vaccines before distributing them locally in order to minimize local deaths. We demonstrate that a nondonor-first vaccination policy can, under some circumstances, dramatically delay the emergence of more-contagious variants. Moreover, we find that vaccine distribution is not a zero-sum game between donor and nondonor countries: weighting the objective function even slightly in favor of minimizing total deaths can achieve dramatic reduction in total deaths with only a small increase in donor-country deaths. The insights yielded by this framework can be used to guide equitable vaccine distribution in future pandemics.
Neuron Structure Modeling for Generalizable Remote Physiological Measurement

Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications like health monitoring and emotional analysis more accessible. However, as the BVP signal is easily affected by environmental changes, existing methods struggle to generalize well for unseen domains. In this paper, we systematically address the domain shift problem in the rPPG measurement task. We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes. In light of this, we propose a domain-label-free approach called NEuron STructure modeling (NEST). NEST improves the generalization capacity by maximizing the coverage of feature space during training, which reduces the chance for under-optimized feature activation during inference. Besides, NEST can also enrich and enhance domain invariant features across multi-domain. We create and benchmark a large-scale domain generalization protocol for the rPPG measurement task. Extensive experiments show that our approach outperforms the state-of-the-art methods on both cross-dataset and intra-dataset settings. The codes are available at https://github.com/LuPaoPao/NEST.
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems

Krylov subspace, which is generated by multiplying a given vector by the matrix of a linear transformation and its successive powers, has been extensively studied in classical optimization literature to design algorithms that converge quickly for large linear inverse problems. For example, the conjugate gradient method (CG), one of the most popular Krylov subspace methods, is based on the idea of minimizing the residual error in the Krylov subspace. However, with the recent advancement of high-performance diffusion solvers for inverse problems, it is not clear how classical wisdom can be synergistically combined with modern diffusion models. In this study, we propose a novel and efficient diffusion sampling strategy that synergistically combines the diffusion sampling and Krylov subspace methods. Specifically, we prove that if the tangent space at a denoised sample by Tweedie's formula forms a Krylov subspace, then the CG initialized with the denoised data ensures the data consistency update to remain in the tangent space. This negates the need to compute the manifold-constrained gradient (MCG), leading to a more efficient diffusion sampling method. Our method is applicable regardless of the parametrization and setting (i.e., VE, VP). Notably, we achieve state-of-the-art reconstruction quality on challenging real-world medical inverse imaging problems, including multi-coil MRI reconstruction and 3D CT reconstruction. Moreover, our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method. Code is available at https://github.com/HJ-harry/DDS
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively.
Metric dimension in a prime ideal sum graph of a commutative ring

The prime ideal sum graph of a commutative unital ring $R$, denoted by $PIS(R)$, is an undirect and simple graph whose vertices are non-trivial ideals of $R$ and there exists and edge between to distinct vertices if and only if their sum is a prime ideal of $R$. In this paper, the metric dimension of $PIS(R)$ is discussed and some formulae for this parameter in $PIS(R)$ are given.
Size effect and scaling in quasi‐static and fatigue fracture of graphene polymer nanocomposites

This work investigated how the structure size affects the quasi-static and fatigue behaviors of graphene polymer nanocomposites, a topic that has been often overlooked. The results showed that both quasi-static and fatigue failure of these materials scale nonlinearly with the structure size due to the presence of a significant Fracture Process Zone (FPZ) ahead of the crack tip induced by graphene nanomodification. Such a complicated size effect and scaling in either quasi-static or fatigue scenario cannot be described by the Linear Elastic Fracture Mechanics (LEFM), but can be well captured by the Size Effect Law (SEL) which considers the FPZ. Thanks to the SEL, the enhanced quasi-static and fatigue fracture properties were properly characterized and shown to be independent of the structure size. In addition, the differences on the morphological and mechanical behaviors between quasi-static fracture and fatigue fracture were also identified and clarified in this work. The experimental data and analytical analyses reported in this paper are important to deeply understand the mechanics of polymer-based nanocomposite materials and even other quasi-brittle materials (e.g., fiber-reinforced polymers or its hybrid with nanoparticles, etc.), and further advance the development of computational models capable of capturing size-dependent fracture of materials in various loading conditions.
Training, Job Satisfaction, POS and Service Quality: The Case of Malaysia

This study is conducted to examine the role of job satisfaction and perceived organizational support as a mediator in the relationship between training and service quality. The data of this study has been collected from 222 public sector employees in Malaysia and their supervisor through survey. The results from data analysis shows that both predictions were not supported by the analysis. However, this study found a significant relationship between training and job satisfaction, and between training and perceived organizational support. Although the main objectives of this study was not supported, however, this study provides empirical evidence of the direct effect of training on perceived organizational support and job satisfaction, particularly in the Malaysian public sector context. The results support the importance of training in producing positive work attitude and behavior found in previous studies, and demonstrates the applicability of this management concept to non-Western settings (Malaysia), and to the public sector more generally.