Miracle-dz/UniFAR-FTUs huggingface dataset:
A search for radio pulsars in five nearby supernova remnants

Most neutron stars are expected to be born in supernovae, but only about half of supernova remnants (SNRs) are associated with a compact object. In many cases, a supernova progenitor may have resulted in a black hole. However, there are several possible reasons why true pulsar-SNR associations may have been missed in previous surveys: The pulsar's radio beam may not be oriented towards us; the pulsar may be too faint to be detectable; or there may be an offset in the pulsar position caused by a kick. Our goal is to find new pulsars in SNRs and explore their possible association with the remnant. The search and selection of the remnants presented in this paper was inspired by the non-detection of any X-ray bright compact objects in these remnants when previously studied. Five SNRs were searched for radio pulsars with the Green Bank Telescope at 820 MHz with multiple pointings to cover the full spatial extent of the remnants. A periodicity search plus an acceleration search up to 500 m/s^2 and a single pulse search were performed for each pointing in order to detect potential isolated binary pulsars and single pulses, respectively. No new pulsars were detected in the survey. However, we were able to re-detect a known pulsar, PSR J2047+5029, near SNR G89.0+4.7. We were unable to detect the radio-quiet gamma-ray pulsar PSR J2021+4026, but we do find a flux density limit of 0.08 mJy. Our flux density limits make our survey two to 16 times more sensitive than previous surveys, while also covering the whole spatial extent of the same remnants. We discuss potential explanations for the non-detection of a pulsar in the studied SNRs and conclude that sensitivity is still the most likely factor responsible for the lack of pulsars in some remnants.
Connecting the Gravity Field, Moment of Inertia, and Core Properties in Jupiter through Empirical Structural Models

Constraining Jupiter’s internal structure is crucial for understanding its formation and evolution history. Recent interior models of Jupiter that fit Juno’s measured gravitational field suggest an inhomogeneous interior and potentially the existence of a diluted core. These models, however, strongly depend on the model assumptions and the equations of state used. A complementary modeling approach is to use empirical structural models. These can later be used to reveal new insights into the planetary interior and be compared to standard models. Here we present empirical structural models of Jupiter where the density profile is constructed by piecewise-polytropic equations. With these models we investigate the relation between the normalized moment of inertia (MoI) and the gravitational moments J 2 and J 4. Given that only the first few gravitational moments of Jupiter are measured with high precision, we show that an accurate and independent measurement of the MoI value could be used to further constrain Jupiter’s interior. An independent measurement of the MoI with an accuracy better than ∼0.1% could constrain Jupiter’s core region and density discontinuities in its envelope. We find that models with a density discontinuity at ∼1 Mbar, as would produce a presumed hydrogen–helium separation, correspond to a fuzzy core in Jupiter. We next test the appropriateness of using polytropes, by comparing them with empirical models based on polynomials. We conclude that both representations result in similar density profiles and ranges of values for quantities like core mass and MoI.
Insights from exact exchange-correlation kernels

The exact exchange-correlation (xc) kernel $f_\mathrm{xc}(x,x',\omega)$ of linear response time-dependent density functional theory is computed over a wide range of frequencies, for three canonical one-dimensional finite systems. Methods used to ensure the numerical robustness of $f_\mathrm{xc}$ are set out. The frequency dependence of $f_\text{xc}$ is found to be due largely to its analytic structure, i.e. its singularities at certain frequencies, which are required in order to capture particular transitions, including those of double excitation character. However, within the frequency range of the first few interacting excitations, $f_\mathrm{xc}$ is approximately frequency independent, meaning the exact adiabatic approximation $f_\mathrm{xc}(\omega = 0)$ remedies the failings of the local density approximation and random phase approximation for these lowest transitions. The key differences between the exact $f_\mathrm{xc}$ and its common approximations are analyzed, and cannot be eliminated by exploiting the limited gauge freedom in $f_\mathrm{xc}$. The optical spectrum benefits from using as accurate as possible an $f_\mathrm{xc}$ and ground-state xc potential, while maintaining exact compatibility between the two is of less importance.
Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

In this work, we present a new semantic segmentation model for historical city maps that surpasses the state of the art in terms of flexibility and performance. Research in automatic map processing is largely focused on homogeneous corpora or even individual maps, leading to inflexible algorithms. Recently, convolutional neural networks have opened new perspectives for the development of more generic tools. Based on two new maps corpora, the first one centered on Paris and the second one gathering cities from all over the world, we propose a method for operationalizing the figuration based on traditional computer vision algorithms that allows large-scale quantitative analysis. In a second step, we propose a semantic segmentation model based on neural networks and implement several improvements. Finally, we analyze the impact of map figuration on segmentation performance and evaluate future ways to improve the representational flexibility of neural networks. To conclude, we show that these networks are able to semantically segment map data of a very large figurative diversity with efficiency.
Cosmology with LIGO/Virgo dark sirens: Hubble parameter and modified gravitational wave propagation

We present a detailed study of the methodology for correlating `dark sirens' (compact binaries coalescences without electromagnetic counterpart) with galaxy catalogs. We propose several improvements on the current state of the art, and we apply them to the GWTC-2 catalog of LIGO/Virgo gravitational wave (GW) detections, and the GLADE galaxy catalog, performing a detailed study of several sources of systematic errors that, with the expected increase in statistics, will eventually become the dominant limitation. We provide a measurement of H 0 from dark sirens alone, finding as the best result H 0 = 67.3+27.6 -17.9 km s-1 Mpc-1 (68% c.l.) which is, currently, the most stringent constraint obtained using only dark sirens. Combining dark sirens with the counterpart for GW170817 we find H 0 = 72.2+13.9 -7.5 km s-1 Mpc-1. We also study modified GW propagation, which is a smoking gun of dark energy and modifications of gravity at cosmological scales, and we show that current observations of dark sirens already start to provide interesting limits. From dark sirens alone, our best result for the parameter Ξ0 that measures deviations from GR (with Ξ0 = 1 in GR) is Ξ0 = 2.1+3.2 -1.2. We finally discuss limits on modified GW propagation under the tentative identification of the flare ZTF19abanrhr as the electromagnetic counterpart of the binary black hole coalescence GW190521, in which case our most stringent result is Ξ0 = 1.8+0.9 -0.6. We release the publicly available code DarkSirensStat, which is available under open source license at https://github.com/CosmoStatGW/DarkSirensStat.
Quantum electrodynamical formulation of photochemical acid generation and its implications on optical lithography

The photochemical acid generation is reﬁned from the very ﬁrst principles of elementary particle physics. We ﬁrst brieﬂy review the formulation of the quantum theory of light based on the quantum electrodynamics framework to establish the probability for acid generation at a given spacetime point. The quantum mechanical acid generation is then combined with the deprotection mechanism to obtain the probabilistic description of the deprotection density, directly related to the feature formation in a photoresist. A statistical analysis of the random deprotection density is presented to reveal the leading characteristics of stochastic feature formation.
Flavonol Glycosides from the Leaves of Embelia keniensis

Five new flavonol glycosides characterized as syringetin 3-O-a-rhamnoside-7-O-β-glucoside, syringetin 3-O-α-rhamnoside-7,4'-di-O-β-glucoside, quercetin-7-O-β-galactosyl (1→3)-β-galactoside, myricetin 3-O-α-rhamnosyl (1→4)-β-galactoside and myricetin 3-O-β-glucosyl (1→2)-β-glucoside-7-O-β-glucosyl-(1→4)-α-rhamnoside have been isolated from a methanolic extract of Embelia keniensis leaves. Known flavonols isolated from the same extract included myricetin, quercetin, kaempferol, myricetin 3-O-α-rhamnoside, myricetin 3-O-β-glucoside, quercetin 3-O-α-rhamnoside, quercetin 3-O-β-glucoside, quercetin 3-O-β-xyloside, isorhamnetin 3-O-α-rhamnoside and myricetin 3-O-rutinoside. Their structures were established from extensive spectroscopic and chemical studies and by comparison with authentic samples.
Dark acoustic oscillations: imprints on the matter power spectrum and the halo mass function

Many non-minimal dark matter scenarios lead to oscillatory features in the matter power spectrum induced by interactions either within the dark sector or with particles from the standard model. Observing such dark acoustic oscillations would therefore be a major step towards understanding dark matter. We investigate what happens to oscillatory features during the process of nonlinear structure formation. We show that at the level of the power spectrum, oscillations are smoothed out by nonlinear mode coupling, gradually disappearing towards lower redshifts. In the halo mass function, however, the same oscillations remain visible until the present epoch. As a consequence, dark acoustic oscillations could be detectable in observations that are either based on the halo mass function or on the high-redshift power spectrum. We investigate the effect of such oscillations on different observables, namely, the cluster mass function, the stellar-to-halo mass relation, and the Lyman-$\alpha$ flux power spectrum. We find that dark acoustic oscillations remain visible in all of these observables, but they are very extended and of low amplitude, making it challenging to detect them as distinct features in the data.
A sub-Neptune and a non-transiting Neptune-mass companion unveiled by ESPRESSO around the bright late-F dwarf HD 5278 (TOI-130)

Context. Transiting sub-Neptune-type planets, with radii approximately between 2 and 4 R⊕, are of particular interest as their study allows us to gain insight into the formation and evolution of a class of planets that are not found in our Solar System. Aims. We exploit the extreme radial velocity (RV) precision of the ultra-stable echelle spectrograph ESPRESSO on the VLT to unveil the physical properties of the transiting sub-Neptune TOI-130 b, uncovered by the TESS mission orbiting the nearby, bright, late F-type star HD 5278 (TOI-130) with a period of Pb = 14.3 days. Methods. We used 43 ESPRESSO high-resolution spectra and broad-band photometry information to derive accurate stellar atmospheric and physical parameters of HD 5278. We exploited the TESS light curve and spectroscopic diagnostics to gauge the impact of stellar activity on the ESPRESSO RVs. We performed separate as well as joint analyses of the TESS photometry and the ESPRESSO RVs using fully Bayesian frameworks to determine the system parameters. Results. Based on the ESPRESSO spectra, the updated stellar parameters of HD 5278 are Teff = 6203 ± 64 K, log g = 4.50 ± 0.11 dex, [Fe/H] = −0.12 ± 0.04 dex, M⋆ = 1.126−0.035+0.036 M⊙, and R⋆ = 1.194−0.016+0.017 R⊙. We determine HD 5278 b’s mass and radius to be Mb = 7.8−1.4+1.5 M⊕ and Rb = 2.45 ± 0.05R⊕. The derived mean density, ϱb = 2.9−0.5+0.6 g cm−3, is consistent with the bulk composition of a sub-Neptune with a substantial (~ 30%) water mass fraction and with a gas envelope comprising ~17% of the measured radius. Given the host brightness and irradiation levels, HD 5278 b is one of the best targetsorbiting G-F primaries for follow-up atmospheric characterization measurements with HST and JWST. We discover a second, non-transiting companion in the system, with a period of Pc = 40.87−0.17+0.18 days and a minimum mass of Mc sin ic = 18.4−1.9+1.8 M⊕. We study emerging trends in parameters space (e.g., mass, radius, stellar insolation, and mean density) of the growing population of transiting sub-Neptunes, and provide statistical evidence for a low occurrence of close-in, 10 − 15M⊕ companions around G-F primaries with Teff ≳ 5500 K.
Does injecting linguistic structure into language models lead to better alignment with brain recordings?

Neuroscientists evaluate deep neural networks for natural language processing as possible candidate models for how language is processed in the brain. These models are often trained without explicit linguistic supervision, but have been shown to learn some linguistic structure in the absence of such supervision (Manning et al., 2020), potentially questioning the relevance of symbolic linguistic theories in modeling such cognitive processes (Warstadt and Bowman, 2020). We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms. Using structure from dependency or minimal recursion semantic annotations, we find alignments improve significantly for one of the datasets. For another dataset, we see more mixed results. We present an extensive analysis of these results. Our proposed approach enables the evaluation of more targeted hypotheses about the composition of meaning in the brain, expanding the range of possible scientific inferences a neuroscientist could make, and opens up new opportunities for cross-pollination between computational neuroscience and linguistics.
Few-Shot Learning for Road Object Detection

Few-shot learning is a problem of high interest in the evolution of deep learning. In this work, we consider the problem of few-shot object detection (FSOD) in a real-world, class-imbalanced scenario. For our experiments, we utilize the India Driving Dataset (IDD), as it includes a class of less-occurring road objects in the image dataset and hence provides a setup suitable for few-shot learning. We evaluate both metric-learning and meta-learning based FSOD methods, in two experimental settings: (i) representative (same-domain) splits from IDD, that evaluates the ability of a model to learn in the context of road images, and (ii) object classes with less-occurring object samples, similar to the open-set setting in real-world. From our experiments, we demonstrate that the metric-learning method outperforms meta-learning on the novel classes by (i) 11.2 mAP points on the same domain, and (ii) 1.0 mAP point on the open-set. We also show that our extension of object classes in a real-world open dataset offers a rich ground for few-shot learning studies.
Hyperpolarisation of external nuclear spins using nitrogen-vacancy centre ensembles

A. J. Healey, 2 L. T. Hall, G. A. L. White, T. Teraji, M. -A. Sani, F. Separovic, J.-P. Tetienne, 2, ∗ and L. C. L. Hollenberg 2, † School of Physics, University of Melbourne, VIC 3010, Australia Centre for Quantum Computation and Communication Technology, School of Physics, University of Melbourne, VIC 3010, Australia National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan School of Chemistry, Bio21 Institute, University of Melbourne, VIC 3010, Australia
Feedback in Forming Star Clusters: The Mass–Radius Relation and Mass Function of Molecular Clumps in the Large Magellanic Cloud

We derive the mass–radius relation and mass function of molecular clumps in the Large Magellanic Cloud (LMC) and interpret them in terms of the simple feedback model proposed by Fall et al. (FKM). Our work utilizes the dendrogram-based catalog of clumps compiled by Wong et al. from 12CO and 13CO maps of six giant molecular clouds in the LMC observed with the Atacama Large Millimeter Array. The Magellanic Clouds are the only external galaxies for which this type of analysis is possible at the necessary spatial resolution (∼1 pc). We find that the mass–radius relation and mass function of LMC clumps have power-law forms, R ∝ M α and , with indices α = 0.36 ± 0.03 and β = −1.8 ± 0.1 over the mass ranges 102 M ⊙ ≲ M ≲ 105 M ⊙ and 102 M ⊙ ≲M ≲ 104 M ⊙, respectively. With these values of α and β for the clumps (i.e., protoclusters), the predicted index for the mass function of young LMC clusters from the FKM model is β ≈ 1.7, in good agreement with the observed index. The situation portrayed here for clumps and clusters in the LMC replicates that in the Milky Way.
Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks

In this paper, we introduce the Layer-Peeled Model , a nonconvex yet analytically tractable optimization program, in a quest to better understand deep neural networks that are trained for a suﬃciently long time. As the name suggests, this new model is derived by isolating the topmost layer from the remainder of the neural network, followed by imposing certain constraints separately on the two parts of the network. We demonstrate that the Layer-Peeled Model, albeit simple, inherits many characteristics of well-trained neural networks, thereby oﬀering an eﬀective tool for explaining and predicting common empirical patterns of deep learning training. First, when working on class-balanced datasets, we prove that any solution to this model forms a simplex equiangular tight frame, which in part explains the recently discovered phenomenon of neural collapse in deep learning training [PHD20]. Moreover, when moving to the imbalanced case, our analysis of the Layer-Peeled Model reveals a hitherto unknown phenomenon that we term Minority Collapse , which fundamentally limits the performance of deep learning models on the minority classes. In addition, we use the Layer-Peeled Model to gain insights into how to mitigate Minority Collapse. Interestingly, this phenomenon is ﬁrst predicted by the Layer-Peeled Model before its conﬁrmation by our computational experiments.
Apolipoprotein E phenotyping from serum by Western blotting

A new method for the apolipoprotein E (apo E) phenotyping directly from human serum has been developed. The method is based on isoelectric focusing of delipidated serum in vertical slab gel systems followed by blotting and immunostaining using polyclonal anti‐apo E antibodies as the first antibody. Apo E phenotyping with this method in 150 serum samples gave exactly the same results as obtained with the conventional method based on isoelectric focusing of delipidated very low density lipoproteins isolated by ultracentrifugation and detected by protein staining. Compared with the conventional method, the present method is less expensive, suitable for large‐scale screening purposes e. g. diagnosis of type III hyperlipidemia and needs only a few microliters of serum. By application for screening of hyperlipidemic sera, we were able to detect two patients with an apo E‐1 variant and two others with “new” E‐3 and E‐4 mutants by the described methods. In addition apparent molecular weight variants of apo E could be detected by sodium dodecyl sulfate‐gel electrophoresis of sera followed by blotting and immunostaining.
Hybrid Parallel Compliance Allows Robots to Operate With Sensorimotor Delays and Low Control Frequencies

Animals locomote robustly and agile, albeit significant sensorimotor delays of their nervous system and the harsh loading conditions resulting from repeated, high-frequent impacts. The engineered sensorimotor control in legged robots is implemented with high control frequencies, often in the kilohertz range. Consequently, robot sensors and actuators can be polled within a few milliseconds. However, especially at harsh impacts with unknown touch-down timing, controllers of legged robots can become unstable, while animals are seemingly not affected. We examine this discrepancy and suggest and implement a hybrid system consisting of a parallel compliant leg joint with varying amounts of passive stiffness and a virtual leg length controller. We present systematic experiments both in computer simulation and robot hardware. Our system shows previously unseen robustness, in the presence of sensorimotor delays up to 60 ms, or control frequencies as low as 20 Hz, for a drop landing task from 1.3 leg lengths high and with a compliance ratio (fraction of physical stiffness of the sum of virtual and physical stiffness) of 0.7. In computer simulations, we report successful drop-landings from 3.8 leg lengths (1.2 m) for a 2 kg quadruped robot with 100 Hz control frequency and a sensorimotor delay of 35 ms.
The Discovery of a Low-energy Excess in Cosmic-Ray Iron: Evidence of the Past Supernova Activity in the Local Bubble

Since its launch, the Alpha Magnetic Spectrometer—02 (AMS-02) has delivered outstanding quality measurements of the spectra of cosmic-ray (CR) species ( , e ±, and nuclei, 1H–8O, 10Ne, 12Mg, 14Si) which resulted in a number of breakthroughs. One of the latest long-awaited surprises is the spectrum of 26Fe just published by AMS-02. Because of the large fragmentation cross section and large ionization energy losses, most of CR iron at low energies is local and may harbor some features associated with relatively recent supernova (SN) activity in the solar neighborhood. Our analysis of the new AMS-02 results, together with Voyager 1 and ACE-CRIS data, reveals an unexpected bump in the iron spectrum and in the Fe/He, Fe/O, and Fe/Si ratios at 1–2 GV, while a similar feature in the spectra of He, O, and Si and in their ratios is absent, hinting at a local source of low-energy CRs. The found excess extends the recent discoveries of radioactive 60Fe deposits in terrestrial and lunar samples and in CRs. We provide an updated local interstellar spectrum (LIS) of iron in the energy range from 1 MeV nucleon−1 to ∼10 TeV nucleon−1. Our calculations employ the GalProp–HelMod framework, which has proved to be a reliable tool in deriving the LIS of CR , e −, and nuclei Z ≤ 28.
Effect of Boehmite Crystallite Size and Steaming on Alumina Properties

Boehmite and pseudoboehmite were studied in order to find the effect of their individual properties upon the properties of transitional alumina and α-Al2O3 produced thereof; the influence of steaming on these properties was also considered. Samples were characterized by X-ray powder diffraction, crystalline structure refinement, scanning electron microscopy, thermoanalysis, and nitrogen adsorption. Boehmite and pseudoboehmite had the same crystalline structure but different crystallite dimensions. Differences between lattice parameters (only 0.0052 nm along the b axis) are so small that the assumption of many authors that pseudoboehmite has intercalated water in its crystalline structure cannot be supported. Small crystallites with larger active areas, increased by crystal faces perpendicular to (020) planes, explain the larger amounts of desorbed water that pseudoboehmite exhibits compared to boehmite while drying. The hydrogen-bond length in boehmite depends on the crystallite size and determines the te.