Miracle-dz/UniFAR-FTUs huggingface dataset:
Dominance of Bursty over Steady Heating of the 4–8 MK Coronal Plasma in a Solar Active Region: Quantification Using Maps of Minimum, Maximum, and Average Brightness

A challenge in characterizing active region (AR) coronal heating is in separating transient (bursty) loop heating from the diffuse background (steady) heating. We present a method of quantifying coronal heating’s bursty and steady components in ARs, applying it to Fe xviii (hot 94) emission of an AR observed by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. The maximum-, minimum-, and average-brightness values for each pixel, over a 24 hr period, yield a maximum-brightness map, a minimum-brightness map, and an average-brightness map of the AR. Running sets of such three maps come from repeating this process for each time step of running windows of 20, 16, 12, 8, 5, 3, 1, and 0.5 hr. From each running window’s set of three maps, we obtain the AR’s three corresponding luminosity light curves. We find (1) the time-averaged ratio of minimum-brightness-map luminosity to average-brightness-map luminosity increases as the time window decreases, and the time-averaged ratio of maximum-brightness-map luminosity to average-brightness-map luminosity decreases as the window decreases; (2) for the 24 hr window, the minimum-brightness map’s luminosity is 5% of the average-brightness map’s luminosity, indicating that at most 5% of the AR’s hot 94 luminosity is from heating that is steady for 24 hr; (3) this upper limit on the fraction of the hot 94 luminosity from steady heating increases to 33% for the 30 minute running window. This requires that the heating of the 4–8 MK plasma in this AR is mostly in bursts lasting less than 30 minutes: at most a third of the heating is steady for 30 minutes.
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning

We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks. Analysis of the internal representation reveals that the pre-trained Transformers acquire largely different representations before and after pre-training, but acquire less information of data in fine-tuning than the randomly initialized one. A closer look at the parameter changes of the pre-trained Transformers reveals that their parameters do not change that much and that the bad performance of the model pre-trained with image data could partially come from large gradients and gradient clipping. To study what information the Transformer pre-trained with language data utilizes, we fine-tune this model with no context provided, finding that the model learns efficiently even without context information. Subsequent follow-up analysis supports the hypothesis that pre-training with language data is likely to make the Transformer get context-like information and utilize it to solve the downstream task.
Virtual Service Embedding With Time-Varying Load and Provable Guarantees

Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services’ virtual network functions (VNFs) at different locations of the cellular infrastructure and manage such instances by scaling them as needed. In this work, we address the above problem and the very relevant aspect of sub-slice reuse among different services. Further, unlike prior art, we account for the services’ finite lifetime and time-varying traffic load. We identify two major sources of inefficiency in service management: (i) the overspending of computing resources due to traffic of multiple services with different latency requirements being processed by the same virtual machine (VM), and (ii) the poor packing of traffic processing requests in the same VM, leading to opening more VMs than necessary. To cope with the above issues, we devise an algorithm, called REShare, that can dynamically adapt to the system's operational conditions and find an optimal trade-off between the aforementioned opposite requirements. We prove that REShare has low algorithmic complexity and is asymptotic 2-competitive under a non-decreasing load. Numerical results, leveraging real-world scenarios, show that our solution outperforms alternatives, swiftly adapting to time-varying conditions and reducing service cost by over 25%.
Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present <italic>X-DualNet</italic>, a simple yet effective approach that enables unsupervised 3D pose transfer. In <italic>X-DualNet</italic>, we introduce a generator <inline-formula><tex-math notation="LaTeX">$G$</tex-math><alternatives><mml:math><mml:mi>G</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq1-3259059.gif"/></alternatives></inline-formula> which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With <inline-formula><tex-math notation="LaTeX">$G$</tex-math><alternatives><mml:math><mml:mi>G</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq2-3259059.gif"/></alternatives></inline-formula> as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.
Randomised subspace methods for non-convex optimization, with applications to nonlinear least-squares

We propose a general random subspace framework for unconstrained nonconvex optimization problems that requires a weak probabilistic assumption on the subspace gradient, which we show to be satisfied by various random matrix ensembles, such as Gaussian and sparse sketching, using Johnson-Lindenstrauss embedding properties. We show that, when safeguarded with trust region or quadratic regularization, this random subspace approach satisfies, with high probability, a complexity bound of order $\mathcal{O}(\epsilon^{-2})$ to drive the (full) gradient below $\epsilon$; matching in the accuracy order, deterministic counterparts of these methods and securing almost sure convergence. Furthermore, no problem dimension dependence appears explicitly in the projection size of the sketching matrix, allowing the choice of low-dimensional subspaces. We particularise this framework to Random Subspace Gauss-Newton (RS-GN) methods for nonlinear least squares problems, that only require the calculation of the Jacobian in the subspace; with similar complexity guarantees. Numerical experiments with RS-GN on CUTEst nonlinear least squares are also presented, with some encouraging results.
Informative Sample-Aware Proxy for Deep Metric Learning

Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding space, receive updates based on proxy-sample similarities in a similar manner to sample representations. In existing methods, a relatively small number of samples can produce large gradient magnitudes (i.e., hard samples), and a relatively large number of samples can produce small gradient magnitudes (i.e., easy samples); these can play a major part in updates. Assuming that acquiring too much sensitivity to such extreme sets of samples would deteriorate the generalizability of a method, we propose a novel proxy-based method called Informative Sample-Aware Proxy (Proxy-ISA), which directly modifies a gradient weighting factor for each sample using a scheduled threshold function, so that the model is more sensitive to the informative samples. Extensive experiments on the CUB-200-2011, Cars-196, Stanford Online Products and In-shop Clothes Retrieval datasets demonstrate the superiority of Proxy-ISA compared with the state-of-the-art methods.
Pressure-Induced Superconductivity in PdTeI with Quasi-One-Dimensional PdTe Chains

The quasi-one-dimensional material PdTeI exhibits unusual electronic transport properties at ambient pressure. Here, we systematically investigate both the structural and electronic responses of PdTeI to external pressure, through a combination of electrical transport, synchrotron X-ray diffraction (XRD), and Raman spectroscopy measurements. The charge density wave (CDW) order in PdTeI is fragile and the transition temperature TCDW decreases rapidly with the application of external pressure. The resistivity hump is indiscernible when the pressure is increased to ~1 GPa. Upon further compression, the resistivity dropping is observed approximately ~15 GPa and zero resistance is established above ~20 GPa, suggesting the occurrence of superconductivity. Combined XRD and Raman data evidence that the emergence of superconductivity is accompanied by a pressure-induced amorphization of PdTeI.
The global structure of a spatial model of infectious disease

In this paper we study an SI model of infectious diseases that takes into account spatial inhomogeneities, resulting in a system of reaction–convection–diffusion equations on a bounded domain. The convection process is included to account for social interaction, as modelled by the location of a focal point or den where the population will tend to aggregate. We show that a vertical bifurcation of steady–state solutions occurs in this model when birth rate is taken as the bifurcation parameter, from which emanates a global secondary branch, which then bifurcates at infinity. Subsequently, we use singular perturbation techniques to give a description of the limiting spatial structure along this branch in large and small parameter limits. Finally, the results are illustrated numerically on some biologically relevant cases.
On the magnetic field evolution time-scale in superconducting neutron star cores

AP acknowledges support from the European Union under the Marie Sklodowska Curie Actions Individual Fellowship, grant agreement no 656370. This work is supported in part by the Spanish MINECO grant AYA2015-66899-C2-2-P, and by the New Compstar COST action MP1304.
On the variation and growth of wave-slope spectra in the capillary-gravity range with increasing wind

A new laser device has been used to make direct wave-slope measurements in the capillary-gravity range. Owing to the design principles, the digital nature of the system and the use of a laser beam as a probe, the earlier problems of intensity variations and meniscus effects were avoided. Using this new technique, wave-slope spectra both down and across the channel were obtained for different wind conditions, along with corresponding mean-square slope values. Comparisons are made with existing data. The results indicate that a quasi-equilibrium state may exist for each wind speed and that it increases in intensity with increasing wind, which may imply an asymptotic nature for the equilibrium-range coefficient Caa. From the data, two significant frictional velocities, 17.5 and 31 cm/s respectively, are identified as critical values for different ranges of wave development.
Controller order reduction with guaranteed performance via coprime factorization

In this paper the problems of the suboptimal H∞ controller order reduction and strictly positive real (SPR) controller order reduction via coprime factorization are studied. The sufficient conditions to ensure the reduced order controllers also being suboptimal H∞ controllers and SPR are given, respectively. The conditions presented may be considered as frequency weighted model reduction problems. We generalize the result of Cℱ(θ) approach in Goddard (Ph.D. Thesis, Trinity College, Cambridge, 1995) for controller order reduction with an H∞ framework and the relationship between our results and some other existing results is established. Copyright © 2003 John Wiley & Sons, Ltd.
Ionizing spectra of stars that lose their envelope through interaction with a binary companion: role of metallicity

Understanding ionizing fluxes of stellar populations is crucial for various astrophysical problems including the epoch of reionization. Massive short-lived Wolf-Rayet stars are generally considered as the main ionizing sources. We examine the role of less massive stars that lose their envelope through interaction with a companion. We use the evolutionary code MESA and the radiative transfer code CMFGEN to investigate stripped stars as a function of metallicity (Z). We show that typical progenitors, initially 12$M_{\odot}$, produce hot and compact stars (~4$M_{\odot}$, 60-80 kK, ~1$R_{\odot}$) that copiously produce ionizing photons, emitting 60-85% and 30-60% of their energy as HI and HeI ionizing radiation, for Z=0.0001-0.02. This is comparable to a typical massive Wolf-Rayet star, when accounting for their longer lifetimes and the favoring initial mass function. Stripped stars further emit their ionizing photons with a delay (~20Myrs after star formation for this progenitor), allowing time for stellar feedback to disperse the birth clouds. This increases the fraction that can escape and contribute to ionization of the intergalactic medium. We further find that Roche stripping fails to fully remove the H-rich envelope at low Z. This questions the common treatment of stripped stars in rapid population synthesis simulations as pure helium stars. We expect implications for the rate of type Ib/c supernova at low Z and for the advanced evolutionary channels for type Ia supernova and gravitational wave sources. We discuss how the characteristic spectral features of stripped stars can be used to increase the observed sample, which is urgently needed to test the models.
Observed versus expected rates of myocarditis after SARS-CoV-2 vaccination: a population-based cohort study

Background: Postmarketing evaluations have linked myocarditis to SARS-CoV-2 mRNA vaccines. We sought to estimate the incidence of myocarditis after mRNA vaccination against SARS-CoV-2, and to compare the incidence with expected rates based on historical background rates in British Columbia. Methods: We conducted an observational study using population health administrative data from the BC COVID-19 Cohort from Dec. 15, 2020, to Mar. 10, 2022. The primary exposure was any dose of an mRNA vaccine against SARS-CoV-2. The primary outcome was incidence of hospital admission or emergency department visit for myocarditis or myopericarditis within 7 and 21 days postvaccination, calculated as myocarditis rates per 100 000 mRNA vaccine doses, expected rates of myocarditis cases and observedto-expected ratios. We stratified analyses by age, sex, vaccine type and dose number. Results: We observed 99 incident cases of myocarditis within 7 days (0.97 cases per 100 000 vaccine doses; observed v. expected ratio 14.81, 95% confidence interval [CI] 10.83–16.55) and 141 cases within 21 days (1.37 cases per 100 000 vaccine doses; observed v. expected ratio 7.03, 95% CI 5.92–8.29) postvaccination. Cases of myocarditis per 100 000 vaccine doses were higher for people aged 12–17 years (2.64, 95% CI 1.54–4.22) and 18–29 years (2.63, 95% CI 1.94–3.50) than for older age groups, for males compared with females (1.64, 95% CI 1.30–2.04 v. 0.35, 95% CI 0.21–0.55), for those receiving a second dose compared with a third dose (1.90, 95% CI 1.50–2.39 v. 0.76, 95% CI 0.45–1.30) and for those who received the mRNA-1273 (Moderna) vaccine compared with the BNT162b2 (Pfizer-BioNTech) vaccine (1.44, 95% CI 1.06–1.91 v. 0.74, 95% CI 0.56–0.98). The highest observed-to-expected ratio was seen after the second dose among males aged 18–29 years who received the mRNA-1273 vaccine (148.32, 95% CI 95.03–220.69). Interpretation: Although absolute rates of myocarditis were low, vaccine type, age and sex are important factors to consider when strategizing vaccine administration to reduce the risk of postvaccination myocarditis. Our findings support the preferential use of the BNT162b2 vaccine over the mRNA-1273 vaccine for people aged 18–29 years.
ONION: Online Semantic Autoencoder Hashing for Cross-Modal Retrieval

Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash functions, without the ability to efficiently handle the online streaming multimedia data. Online hashing can effectively address the preceding issue by using the online learning scheme to incrementally update the hash functions. Nevertheless, the existing online CMH approaches still suffer from several challenges, such as (1) how to efficiently and effectively utilize the supervision information, (2) how to learn more powerful hash functions, and (3) how to solve the binary constraints. To mitigate these limitations, we present a novel online hashing approach named ONION (ONline semantIc autOencoder hashiNg). Specifically, it leverages the semantic autoencoder scheme to establish the correlations between binary codes and labels, delivering the power to obtain more discriminative hash codes. Besides, the proposed ONION directly utilizes the label inner product to build the connection between existing data and newly coming data. Therefore, the optimization is less sensitive to the newly arriving data. Equipping a discrete optimization scheme designed to solve the binary constraints, the quantization errors can be dramatically reduced. Furthermore, the hash functions are learned by the proposed autoencoder strategy, making the hash functions more powerful. Extensive experiments on three large-scale databases demonstrate that the performance of our ONION is superior to several recent competitive online and offline cross-media algorithms.
Color2Vec: Web-Based Modeling of Word-Color Association with Sociocultural Contexts

Color design has long benefited from the statistical analysis of public taste and, more recently, from crowdsourcing to discover fresh and popular ideas. However, the current color dictionary is considerably restricted in terms of the scope of expressible design concepts and the control of target demographics. We propose a search-engine-based color palette generator inspired by Natural Language Processing algorithms that filter and cluster semantically related words. The post-evaluation reveals that our results not only faithfully realize the given keywords but are notable indicators of inter-group dynamics; the differential recognition of the other group's identity colors reflects the direction of historic, geographic, or cultural influence.
AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: perpendicular flows of gas and dark matter

AMI observations towards CIZA J2242+5301, in comparison with observations of weak gravitational lensing and X-ray emission from the literature, are used to investigate the behaviour of non-baryonic dark matter (NBDM) and gas during the merger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence of high pressure gas elongated perpendicularly to the X-ray and weak-lensing morphologies which, given the merger-axis constraints in the literature, implies that high pressure gas is pushed out into a linear structure during core passing. Simulations in the literature closely matching the inferred merger scenario show the formation of gas density and temperature structures perpendicular to the merger axis. These SZ observations are challenging for modified gravity theories in which NBDM is not the dominant contributor to galaxy-cluster gravity.
Publications of the Astronomical Society of the Pacific 109: 645-658, 1997 June BVRIJHK Photometry of Cepheid Variables

Contemporaneous BVRI and JHK photometry are presented for 22 Cepheid variables. Nineteen of these variables also have uvby photometry available, making them excellent candidates for application of the Infrared Flux Method of distance determination. We are in the process of acquiring high-precision radial velocities of 16 of these variables in anticipation of conducting that analysis.
Macroeconomic Shocks and Employment in sub-Sharan Africa: Do Labour Market Institutions Matter?

Abstract The effects of macroeconomic shocks and labour market institutions on employment in sub-Saharan African countries are examined in this study. Using a sample of 27 SSA countries for the period 2007 to 2018, both linear and interactive relationships are investigated. The results show that labour market institutions (especially in terms of wage flexibility) dampen the effects of shocks on modern employment but amplify the effects of shocks on informal employment in the sampled SSA countries. There is also evidence that shocks themselves (especially those emanating from the external sector) do not matter for a huge proportion of employment changes in SSA countries. Rather, the direct effects of shocks on employment are more profound in the formal sector. The study therefore concludes that reforming the informal sector will help to ensure the effectiveness of labour market institutions in mitigating the negative impacts of external shocks on employment in SSA.
Common Envelope Wind Tunnel: Coefficients of Drag and Accretion in a Simplified Context for Studying Flows around Objects Embedded within Stellar Envelopes

This paper examines the properties of flows around objects embedded within common envelopes in the simplified context of a “wind tunnel. ” We establish characteristic relationships between key common envelope flow parameters like the Mach number and density scale height. Our wind tunnel is a three-dimensional, Cartesian geometry hydrodynamic simulation setup that includes the gravity of the primary and secondary stars and allows us to study the coefficients of drag and accretion experienced by the embedded object. Accretion and drag lead to a transformation of an embedded object and its orbit during a common envelope phase. We present two suites of simulations spanning a range of density gradients and Mach numbers—relevant for flow near the limb of a stellar envelope to the deep interior. In one suite, we adopt an ideal gas adiabatic exponent of , in the other, . We find that coefficients of drag rise in flows with steeper density gradients and that coefficients of drag and accretion are consistently higher in the more compressible, flow. We illustrate the impact of these newly derived coefficients by integrating the inspiral of a secondary object through the envelopes of ( ) and ( ) giants. In these examples, we find a relatively rapid initial inspiral because, near the stellar limb, dynamical friction drag is generated mainly from dense gas focused from deeper within the primary-star’s envelope. This rapid initial inspiral timescale carries potential implications for the timescale of transients from early common envelope interaction.