Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of stable binary neutron star merger remnants: A case study

In this work, we study the merger of two neutron stars with a gravitational mass of 1.4 M_sol each, employing the Shen-Horowitz-Teige equation of state. This equation of state is a corner case, allowing the formation of a stable neutron star with the given total baryonic mass of 3.03 M_sol. We investigate in unprecedented detail the structure of the remnant, in particular the mass distribution, the thermal structure, and the rotation profile. We also compute fluid trajectories both inside the remnant and those relevant for the formation of the disk. We find a peanut-shaped fluid flow inside the remnant following a strong m=2 perturbation. Moreover, the flow is locally compressive, causing the appearance of dynamic hot spots. Further, we introduce new diagnostic measures that are easy to implement in numeric simulations and that allow to quantify mass and compactness of merger remnants in a well-defined way. As in previous studies of supra- and hypermassive stars, we find a remnant with a slowly rotating core and an outer envelope rotating at nearly Keplerian velocity. We compute a Tolman-Oppenheimer-Volkoff star model which agrees well with that of the remnant in the core, while the latter possesses extensive outer layers rotating close to Kepler velocity. Finally, we extract the gravitational wave signal and discuss the detectability with modern observatories. This study has implications for the interpretation of gravitational wave detections from the post-merger phase, and is relevant for short gamma-ray burst models.
REVEALING TYPE Ia SUPERNOVA PHYSICS WITH COSMIC RATES AND NUCLEAR GAMMA RAYS

Type Ia supernovae (SNe Ia) remain mysterious despite their central importance in cosmology and their rapidly increasing discovery rate. The progenitors of SNe Ia can be probed by the delay time between progenitor birth and explosion as SNe Ia. The explosions and progenitors of SNe Ia can be probed by MeV nuclear gamma rays emitted in the decays of radioactive nickel and cobalt into iron. We compare the cosmic star formation and SN Ia rates, finding that their different redshift evolution requires a large fraction of SNe Ia to have large delay times. A delay-time distribution of the form t−α with α = 1.0 ± 0.3 provides a good fit, implying that 50% of SNe Ia explode more than ∼1 Gyr after progenitor birth. The extrapolation of the cosmic SN Ia rate to z = 0 agrees with the rate we deduce from catalogs of local SNe Ia. We investigate prospects for gamma-ray telescopes to exploit the facts that escaping gamma rays directly reveal the power source of SNe Ia and uniquely provide tomography of the expanding ejecta. We find large improvements relative to earlier studies by Gehrels et al. in 1987 and Timmes & Woosley in 1997 due to larger and more certain SN Ia rates and advances in gamma-ray detectors. The proposed Advanced Compton Telescope, with a narrow-line sensitivity ∼60 times better than that of current satellites, would, on an annual basis, detect up to ∼100 SNe Ia (3σ) and provide revolutionary model discrimination for SNe Ia within 20 Mpc, with gamma-ray light curves measured with ∼10σ significance daily for ∼100 days. Even more modest improvements in detector sensitivity would open a new and invaluable astronomy with frequent SN Ia gamma-ray detections.
A Continuum Theory of Dynamically Loaded Polymers

A thermo-mechanical continuum theory is proposed for dynamically loaded glassy polymers. The theory is based on an ansatz for the Helmholtz free energy where both the deviatoric and the volumetric contributions to the free energy are rate-dependent. The requirement that the free energy is fully rate dependent arises from the need to model the full range of conditions between those found in quasi-static applications to those common in high-rate shock loading scenarios. Using a purely equilibrium equation of state is found to be insufficient. The resulting model, called the Glassy Amorphous Polymer (GAP) model, suitably captures the thermo-mechanical behavior of both equilibrium properties, but also high-rate phenomena like the shock Hugoniot. Polymethylmethacrylate (PMMA) is used as a representative polymer because it is one of the few polymers where sufficient experiments have been done to determine many of the parameters required by the GAP model. An important part of the GAP model is the Hierarchical Flow Stress (HFS) model, which is shown to accurately represent the stress-strain behavior of PMMA over a broad range of strain rates and temperatures. The HFS model replicates the details of stress plateau, softening, and hardening behavior observed in glassy amorphous polymers. The theory is not restricted to isothermal conditions. Analysis is devoted to several issues associated with a non-equilibrium equation of state (EOS). Because data is insufficient for the full determination of the model's non-equilibrium EOS, several plausible conditions called the quasi-equilibrium hypothesis are put forth to complete the model. Comparisons of experimental and theoretical results over a wide-range of loading rates and conditions are reported.
Galaxy Formation: Where Do We Stand?

This paper presents a review of the topic of galaxy formation and evolution, focusing on basic features of galaxies, and how these observables reveal how galaxies and their stars assemble over cosmic time. I give an overview of the observed properties of galaxies in the nearby universe and for those at higher redshifts up to z ∼ 10. This includes a discussion of the major processes in which galaxies assemble and how we can now observe these - including the merger history of galaxies, the gas accretion and star formation rates. I sh ow that for the most massive galaxies mergers and accretion are about equally important in the galaxy formation process between z = 1− 3, while this likely differs for lower mass systems. I also di scuss the mass differential evolution for galaxies, as well as how environment can affect galaxy evolution, although mass is the primary criteria for driving evolution. I also discus s how we are beginning to measure the dark matter content of galaxies at different epochs as measured through kinematics and clustering. Finally, I review how observables of galaxies, and the observed galaxy formation process, compares with predictions from simulations of galaxy formation, finding significant discrepancies in the abundances of massive galaxies and the merger history. I conclude by examining prospects for the future using JWST, Euclid, SKA, and the ELTs in addressing outstanding issues.
Zoomed cosmological simulations of Milky Way-sized haloes in f(R) gravity

VS and CA would like to thank the Klaus Tschira foundation and acknowledge support from the Deutsche Forschungsgemeinschaft (DFG) through Transregio 33, ‘The Dark Universe’. EP gratefully acknowledges support by the Kavli Foundation and the FP7 European Research Council Advanced Grant Emergence-320596. The authors acknowledge CPU-time from the Juelich Supercomputer Centre on the JURECA system.
Anyonic interferometry without anyons: how a flux qubit can read out a topological qubit

Proposals to measure non-Abelian anyons in a superconductor by quantum interference of vortices suffer from the predominantly classical dynamics of the normal core of an Abrikosov vortex. We show how to avoid this obstruction using coreless Josephson vortices, for which the quantum dynamics has been demonstrated experimentally. The interferometer is a flux qubit in a Josephson junction circuit, which can non-destructively read out a topological qubit stored in a pair of anyons—even though the Josephson vortices themselves are not anyons. The flux qubit does not couple to intra-vortex excitations, thereby removing the dominant restriction on the operating temperature of anyonic interferometry in superconductors.
Directed Multicommodity Flows: Cut-Sufficiency and Forbidden Relevant Minors

In a multicommodity flow problem, the goal is to route paths in a supply graph G to satisfy demands between vertices, represented by a demand graph H. This must be done simultaneously for all commodities, without exceeding edge capacities. To do this, there can be no cut where the requested demand exceeds the available capacity. This is called the cut condition. The Max-Flow Min-Cut Theorem states that for single-commodity flows, the cut condition guarantees that a feasible routing exists. This fails for general multicommodity flows. A supply-demand graph pair (G,H) is cut-sufficient if, for all capacity and demand weights, the cut condition implies feasibility. Many results are known for undirected cut-sufficiency. For instance, the cut-sufficient pairs with series-parallel supply graphs have a forbbiden-minor characterization. However, less is known about cut-sufficiency for directed multicommodity flows. We characterize cut-sufficiency for several classes of directed multicommodity flow problems. We consider pairs with roundtrip or two-path demands, where H is a 2-cycle and 2-path, respectively. We prove that the cut-sufficient pairs in these classes are characterized by one and two forbidden minors, respectively. We also consider pairs with two disjoint demands. For these, we give a forbidden-minor characterization of the pairs that are cut-sufficient for unit demands, and conjecture about their cutsufficiency with arbitrary weights. Unlike the undirected case, for directed graphs the class of cut-sufficient pairs is not closed under taking minors. To solve this, we develop a theory of relevant minors, which restricts permitted contractions to restore the closure of cut-sufficiency. Our characterizations are in terms of forbidden relevant minors. As an application of our results, we show that recognizing cut-sufficiency is NP-hard for roundtrip demands. This contrasts with undirected two-commodity flows, for which supply-demand graph pairs are always cut-sufficient. We also provide a primal proof of the fact that any orientation of an undirected tree is cutsufficient with any directed demand graph. This was previously known, but was proven via the dual method of metric embeddings. We prove trees are the only graphs with this property.
Deep Learning-Based Variable Scaling Beam Training for Massive MIMO mmWave Systems

Improving the accuracy of beam training while reducing the training overhead and the influence of noise has become an important issue for massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) communication systems. In this paper, we propose a deep learning-based multi-scale neural network for beam training in massive MIMO mmWave communication system. The model predicts the orientation of narrow beams by learning the characteristics of wide beams, achieving high accuracy and low training overhead. Specifically, our model consists of three modules. In the first module, we deploy a convolutional neural network (CNN) to extract features of the instantaneous received signal of a wide beam. In the second module, we develop multi-scale convolution to extract wide beam features of the different time combination. In the third module, we conduct a long-term short-term memory (LSTM) network to calibrate the orientation of narrow beams based on previous predictions, thereby enhance the model's robustness to noise. Finally, according to the experimental results, our model improves beam training accuracy with low training overhead while reducing the influence of noise.
Classification of Skin Cancer Segmentation using Hybrid Partial Differential Equation with Fuzzy Clustering based on Machine Learning Techniques

Skin cancer, particularly melanoma skin cancer is a serious public health concern today. The majority of skin cancers may be treated if caught early enough. The fast proliferation of skin cancer necessitates the development of an automatic computerized detection system for skin cancer in its primary phases. The visual qualities of many skin cancer images are similar. The process of extracting characteristics from skin cancer image is a difficult one. The automated computerized diagnosis process aids dermatologists in improving the accuracy of skin disease analysis, allowing them to save diagnostic time and provide better therapy for their patients. This research study presents an intelligent method for detecting malignant and non-cancerous cells using image processing techniques. The Median Filter is first used to reduce artifacts, skin color, hair, and other characteristics of the produced images by removing noise from the skin lesion. The lesion section is then segmented individually using the suggested Hybrid Partial Differential Equation with Fuzzy Clustering (HPDE-FC) technique, which is also effective for feature extraction. Asymmetry, Border, Color and Diameter characteristics are extracted using ABCD scoring approach. Using several machine learning approaches like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF) and Nave Bayes (NB) classifiers, the collected features are immediately fed to classifiers to categorize skin lesions amongst cancerous (malignant) and non-cancerous(melanoma). This research work, 325 images of normal skin lesions as well as 572 images of malignant skin lesions are downloaded from the International Skin Imaging Collaboration (ISIC) for this study. Using SVM classifiers, a classification result of 97.7% accuracy is obtained. Our goal is to evaluate the suggested segmentation technique’s performance, excerpt the most applicable features and associate the categorization results against those of other algorithms in literature.
Spatial Development of Superconductivity in the Sr2RuO4–Ru Eutectic System

We have clarified how the enhanced superconductivity, often referred to as the 3-K phase superconductivity, develops in the Sr 2 RuO 4 –Ru eutectic system. From the detailed ac and dc susceptibility measurements on well-characterized crystals, we revealed strongly anisotropic shielding, governed by the direction of the screening current dominated within the RuO 2 plane rather than by the orientation of the Ru lamellae. The onset temperature of the 3-K phase superconductivity probed by diamagnetic screening is as high as 3.5 K. The temperature dependence of the diamagnetic shielding above around 2 K is well ascribed by the interfacial screening around each Ru lamella. Below around 2 K, the rapid development of the shielding fraction as well as its peculiar response to ac and dc magnetic fields are explained by the formation of the Josephson network consisting of inter-lamellar supercurrents.
Pseudogap formation above the superconducting dome in iron-pnictides

The nature of the pseudogap (PG) in high transition temperature (high ${T}_{c}$) superconducting cuprates has been a major issue in condensed matter physics. It is still unclear whether the high ${T}_{c}$ superconductivity can be universally associated with the PG formation. Here we provide direct evidence of the existence of the PG phase via angle-resolved photoemission spectroscopy in another family of high ${T}_{c}$ superconductor, iron pnictides. Our results reveal a composition-dependent PG formation in the multiband electronic structure of BaFe${}_{2}$(As${}_{1\ensuremath{-}x}$P${}_{x}$)${}_{2}$. The PG develops well above the magnetostructural transition for low $x$, persists above the nonmagnetic superconducting dome for optimal $x$, and is destroyed for $x$ \ensuremath{\sim} 0.6, thus showing a notable similarity with cuprates. In addition, the PG formation is accompanied by inequivalent energy shifts in the $zx/yz$ orbitals of iron atoms, indicative of a peculiar iron orbital ordering which breaks the fourfold rotational symmetry.
Forecast constraints on cosmic strings from future CMB, pulsar timing, and gravitational wave direct detection experiments

We study future observational constraints on cosmic string parameters from various types of next-generation experiments: direct detection of gravitational waves (GWs), pulsar timing array, and the cosmic microwave background (CMB). We consider both GW burst and stochastic GW background searches by ground- and space-based interferometers as well as GW background detection in pulsar timing experiments. We also consider cosmic string contributions to the CMB temperature and polarization anisotropies. These different types of observations offer independent probes of cosmic strings and may enable us to investigate cosmic string properties if the signature is detected. In this paper, we evaluate the power of future experiments to constrain cosmic string parameters, such as the string tension Gmu, the initial loop size alpha, and the reconnection probability p, by performing Fisher information matrix calculations. We find that combining the information from the different types of observations breaks parameter degeneracies and provides more stringent constraints on the parameters. We also find future space-borne interferometers independently provide a highly precise determination of the parameters.
V344 LYRAE: A TOUCHSTONE SU UMa CATACLYSMIC VARIABLE IN THE KEPLER FIELD

We report on the analysis of the Kepler short-cadence (SC) light curve of V344 Lyr obtained during 2009 June 20 through 2010 March 19 (Q2–Q4). The system is an SU UMa star showing dwarf nova (DN) outbursts and superoutbursts, and promises to be a touchstone for CV studies for the foreseeable future. The system displays both positive and negative superhumps with periods of 2.20 and 2.06 hr, respectively, and we identify an orbital period of 2.11 hr. The positive superhumps have a maximum amplitude of ∼0.25 mag, the negative superhumps have a maximum amplitude of ∼0.8 mag, and the orbital period at quiescence has an amplitude of ∼0.025 mag. The quality of the Kepler data is such that we can test vigorously the models for accretion disk dynamics that have been emerging in the past several years. The SC data for V344 Lyr are consistent with the model that two physical sources yield positive superhumps: early in the superoutburst, the superhump signal is generated by viscous dissipation within the periodically flexing disk, but late in the superoutburst, the signal is generated as the accretion stream bright spot sweeps around the rim of the non-axisymmetric disk. The disk superhumps are roughly anti-phased with the stream/late superhumps. The V344 Lyr data also reveal negative superhumps arising from accretion onto a tilted disk precessing in the retrograde direction and suggest that negative superhumps may appear during the decline of DN outbursts. The period of negative superhumps has a positive in between outbursts.
THE METALLICITY DISTRIBUTION FUNCTIONS OF SEGUE G AND K DWARFS: CONSTRAINTS FOR DISK CHEMICAL EVOLUTION AND FORMATION

We present the metallicity distribution function (MDF) for 24,270 G and 16,847 K dwarfs at distances from 0.2 to 2.3 kpc from the Galactic plane, based on spectroscopy from the Sloan Extension for Galactic Understanding and Exploration (SEGUE) survey. This stellar sample is significantly larger in both number and volume than previous spectroscopic analyses, which were limited to the solar vicinity, making it ideal for comparison with local volume-limited samples and Galactic models. For the first time, we have corrected the MDF for the various observational biases introduced by the SEGUE target-selection strategy. SEGUE is particularly notable for its sample of K dwarfs, which are too faint to examine spectroscopically far from the solar neighborhood. The MDF of both spectral types becomes more metal-poor with increasing |Z|, which reflects the transition from a sample with small [α/Fe] values at small heights to one with enhanced [α/Fe] above 1 kpc. Comparison of our SEGUE distributions to those of two different Milky Way models reveals that both are more metal-rich than our observed distributions at all heights above the plane. Our unbiased observations of G and K dwarfs provide valuable constraints over the |Z|-height range of the Milky Way disk for chemical and dynamical Galaxy evolution models, previously only calibrated to the solar neighborhood, with particular utility for thin- and thick-disk formation models.
Recent Advances and Challenges of Magnetic Scaffolds for Tumor Hyperthermia and Tissue Engineering

Biomedicine is seeking multifunctional tools for multimodal effective treatments. Biomaterials functionalized with magnetic nanoparticles are promising candidates for developing innovative tissue engineering devices, through magnetically targeted drug delivery strategies, and for radiofrequency magnetic hyperthermia against tumors. This work deals with the summary of the most recent advances about magnetic scaffolds, while providing theoretical and numerical models for studying their use for biomedical applications, and with the outline of the challenges and future perspective of this class of functional nanocomposite biomaterials.
A Short-term Load Forecasting Method Based on Attention Mechanism of Time Convolution Network

Short-term load forecasting plays an import role in the power system, which has been widely applied in many grid businesses. Most of the existing short-term power load forecasting methods are based on traditional algorithms, which can not make full use of a variety of long time-series information for accurate forecasting. Based on the attention mechanism of deep learning algorithm and time convolution network, a new short-term load forecasting algorithm is proposed in the paper. The algorithm introduces causal convolution to encode features in a long time sequence, and uses attention mechanism to improve the performance of network feature expression. Finally, the different network structures are used to encode features with different information density, which significantly improves the prediction accuracy. The experiments are conducted on the load data of a certain region in China and the results demonstrate that the proposed methods are effective.
Improved stability analysis for Takagi-Sugeno (T-S) fuzzy systems with two additive time-varying delays

This paper focused on the stability analysis criteria for Takagi-Sugeno (T-S) fuzzy systems with two additive time-varying delay. By constructing an appropriate Lyapunov-Krasovskii functional using two additive delay components and combined with the state vector augmentation. Then, by employing the Finsler's lemma and Seuret-Wirtinger's integral inequality, some less conservative delay-dependent stability criteria are obtained in terms of linear matrix inequality (LMIs), which can be solved by using Matlab LMI toolbox. Finally, numerical results are provided to illustrate the effectiveness of the proposed stability criteria.
A Localization Method with UKF/MAF for a Single Antenna GNSS/IMU Integration of Autonomous Unmanned Ground Vehicle

In this paper, we present a localization method for an UGV by using the integration of a single-antenna GNSS and a low cost IMU. An unscented Kalman filter working together with a moving average filter is employed to the method because of limitation on the linear characteristics of the Kalman filter. Experimental test results using a 4-wheel UGV are included in the paper to show the feasibility of the proposed localization method