Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient early stellar feedback can suppress galactic outflows by reducing supernova clustering

We present a novel set of stellar feedback models, implemented in the moving-mesh code arepo, designed for galaxy formation simulations with near-parsec (or better) resolution. These include explicit sampling of stars from the IMF, allowing feedback to be linked to individual massive stars, an improved method for the modelling of H ii regions, photoelectric (PE) heating from a spatially varying FUV field and supernova feedback. We perform a suite of 32 simulations of isolated $M_\mathrm{vir} = 10^{10}\, \mathrm{M_\odot }$ galaxies with a baryonic mass resolution of $20\, \mathrm{M_\odot }$ in order to study the non-linear coupling of the different feedback channels. We find that photoionization (PI) and supernova feedback are both independently capable of regulating star formation to the same level, while PE heating is inefficient. PI produces a considerably smoother star formation history than supernovae. When all feedback channels are combined, the additional suppression of star formation rates is minor. However, outflow rates are substantially reduced relative to the supernova only simulations. We show that this is directly caused by a suppression of supernova clustering by the PI feedback, disrupting star-forming clouds prior to the first supernovae. We demonstrate that our results are robust to variations of our star formation prescription, feedback models and the baryon fraction of the galaxy. Our results also imply that the burstiness of star formation and the mass loading of outflows may be overestimated if the adopted star particle mass is considerably larger than the mass of individual stars because this imposes a minimum cluster size.
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distillation to bridge the gap between a model trained on high-quality inputs at training time and another tested on low-quality inputs at inference time. In particular, we design a two-stage training pipeline for point cloud object detection. First, we train an object detection model on dense point clouds, which are generated from multiple frames using extra information only available at training time. Then, we train the model's identical counterpart on sparse single-frame point clouds with consistency regularization on features from both models. We show that this procedure improves performance on low-quality data during testing, without additional overhead.
Measurement and Entanglement Phase Transitions in All-To-All Quantum Circuits, on Quantum Trees, and in Landau-Ginsburg Theory

Quantum many-body systems subjected to local measurements at a nonzero rate can be in distinct dynamical phases, with differing entanglement properties. We introduce theoretical approaches to measurement-induced phase transitions (MPT) and also to entanglement transitions in random tensor networks. Many of our results are for "all-to-all" quantum circuits with unitaries and measurements, in which any qubit can couple to any other, and related settings where some of the complications of low-dimensional models are reduced. We also propose field theory descriptions for spatially local systems of finite dimensionality. To build intuition, we first solve the simplest "minimal cut" toy model for entanglement dynamics in all-to-all circuits, finding scaling forms and exponents within this approximation. We then show that certain all-to-all measurement circuits allow exact results by exploiting the circuit's local tree-like structure. For this reason, we make a detour to give universal results for entanglement phase transitions in a class of random tree tensor networks, making a connection with the classical theory of directed polymers on a tree. We then compare these results with numerics in all-to-all circuits, both for the MPT and for the simpler "Forced Measurement Phase Transition" (FMPT). We characterize the two different phases in all-to-all circuits using observables that are sensitive to the amount of information propagated between the initial and final time. We demonstrate signatures of the two phases that can be understood from simple models. Finally we propose Landau-Ginsburg-Wilson-like field theories for the MPT, the FMPT, and for entanglement transitions in tensor networks. This analysis shows a surprising difference between the MPT and the other cases. We discuss variants of the measurement problem with additional structure, and questions for the future.
Investigating Applications on the A64FX

The A64FX processor from Fujitsu, being designed for computational simulation and machine learning applications, has the potential for unprecedented performance in HPC systems. In this paper, we evaluate the A64FX by benchmarking against a range of production HPC platforms that cover a number of processor technologies. We investigate the performance of complex scientific applications across multiple nodes, as well as single node and mini-kernel benchmarks. This paper finds that the performance of the A64FX processor across our chosen benchmarks often significantly exceeds other platforms, even without specific application optimisations for the processor instruction set or hardware. However, this is not true for all the benchmarks we have undertaken. Furthermore, the specific configuration of applications can have an impact on the runtime and performance experienced.
The Population III Origin of GW190521

We explore the possibility that the recently detected black hole binary (BHB) merger event GW190521 originates from the first generation of massive, metal-free, so-called Population III (Pop III) stars. Based on improved binary statistics derived from N-body simulations of Pop III star clusters, we calculate the merger rate densities of Pop III BHBs similar to GW190521, in two evolution channels: classical binary stellar evolution and dynamical hardening in high-redshift nuclear star clusters. Both channels can explain the observed rate density. However, the latter is favored by better agreement with observation and less restrictions on uncertain parameters. Our analysis also indicates that given the distinct features of the two channels, future observation of BHB mergers similar to GW190521 with third-generation gravitational wave detectors will greatly improve our knowledge of the evolution of Pop III BHBs, especially for their dynamics during cosmic structure formation.
Observations and Modeling of the Onset of Fast Reconnection in the Solar Transition Region

Magnetic reconnection is a fundamental plasma process that plays a critical role not only in energy release in the solar atmosphere, but also in fusion, astrophysics, and other space plasma environments. One of the challenges in explaining solar observations in which reconnection is thought to play a critical role is to account for the transition of the dynamics from a slow quasi-continuous phase to a fast and impulsive energetic burst of much shorter duration. Despite the theoretical progress in identifying mechanisms that might lead to rapid onset, a lack of observations of this transition has left models poorly constrained. High-resolution spectroscopic observations from NASA's Interface Region Imaging Spectrograph now reveal tell-tale signatures of the abrupt transition of reconnection from a slow phase to a fast, impulsive phase during UV bursts or explosive events in the Sun's atmosphere. Our observations are consistent with numerical simulations of the plasmoid instability, and provide evidence for the onset of fast reconnection mediated by plasmoids and new opportunities for remote-sensing diagnostics of reconnection mechanisms on the Sun.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while multilayer perceptrons (MLPs) do not extrapolate well in certain simple tasks, Graph Neural Network (GNN), a structured network with MLP modules, has shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most non-linear functions. But, they can provably learn a linear target function when the training distribution is sufficiently "diverse". Second, in connection to analyzing successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.
Male investment in the European corn borer, Ostrinia nubilalis (Lepidoptera: Pyralidae): impact on female longevity and reproductive performance

It is generally assumed that the cost of producing ejaculates is relatively low for males, although the decline observed in spermatophore size with repeated mating in certain lepidopteran species would suggest that this is not always the case. However, despite such observed differences in spermatophores very little research has been done to determine if female reproductive success is affected by the previous mating history of her mate. A study was undertaken, using the European corn borer, Ostrinia nubilalis, to determine the extent to which (i) male lifetime reproductive success was related to the number of mates, acquired and the actual number of times that a male mated when provided virgin females daily for a period of 10 days, (ii) spermatophore size changed with successive matings and (iii) previous male mating history affected the longevity and the reproductive performance of singly mated females
Fine-grained complexity of the list homomorphism problem: feedback vertex set and cutwidth

For graphs $G,H$, a homomorphism from $G$ to $H$ is an edge-preserving mapping from $V(G)$ to $V(H)$. In the list homomorphism problem, denoted by \textsc{LHom}($H$), we are given a graph $G$ and lists $L: V(G) \to 2^{V(H)}$, and we ask for a homomorphism from $G$ to $H$ which additionally respects the lists $L$. Very recently Okrasa, Piecyk, and Rzązewski [ESA 2020] defined an invariant $i^*(H)$ and proved that under the SETH $\mathcal{O}^*\left (i^*(H)^{\textrm{tw}(G)}\right)$ is the tight complexity bound for \textsc{LHom}($H$), parameterized by the treewidth $\textrm{tw}(G)$ of the instance graph $G$. We study the complexity of the problem under dirretent parameterizations. As the first result, we show that $i^*(H)$ is also the right complexity base if the parameter is the size of a minimum feedback vertex set of $G$. Then we turn our attention to a parameterization by the cutwidth $\textrm{ctw}(G)$ of $G$. Jansen and Nederlof~[ESA 2018] showed that \textsc{List $k$-Coloring} (i.e., \textsc{LHom}($K_k$)) can be solved in time $\mathcal{O}^*\left (c^{\textrm{ctw}(G)}\right)$ where $c$ does not depend on $k$. Jansen asked if this behavior extends to graph homomorphisms. As the main result of the paper, we answer the question in the negative. We define a new graph invariant $mim^*(H)$ and prove that \textsc{LHom}($H$) problem cannot be solved in time $\mathcal{O}^*\left ((mim^*(H)-\varepsilon)^{\textrm{ctw}(G)}\right)$ for any $\varepsilon >0$, unless the SETH fails. This implies that there is no $c$, such that for every odd cycle the non-list version of the problem can be solved in time $\mathcal{O}^*\left (c^{\textrm{ctw}(G)} \right)$. Finally, we generalize the algorithm of Jansen and Nederlof, so that it can be used to solve \textsc{LHom}($H$) for every graph $H$; its complexity depends on $\textrm{ctw}(G)$ and another invariant of $H$, which is constant for cliques.
Dirac operators on lightlike hypersurfaces

In this study, we obtain a spinorial Gauss formula for a lightlike hypersurface in Lorentzian manifold with 4-dimension. Then, we take into account the changes caused by degenerate metric on hypersurface and investigate Dirac operator for lightlike hypersurface. Later, we establish the relation between Dirac operators and Riemannian curvatures of manifold and hypersurface.
Galaxy-scale ionised winds driven by ultra-fast outflows in two nearby quasars

We used MUSE adaptive optics data in narrow field mode to study the properties of the ionised gas in MR 2251−178 and PG 1126−041, two nearby (z ≃ 0.06) bright quasars (QSOs) hosting sub-pc scale ultra-fast outflows (UFOs) detected in the X-ray band. We decomposed the optical emission from diffuse gas into a low- and a high-velocity components. The former is characterised by a clean, regular velocity field and a low (∼80 km s−1) velocity dispersion. It traces regularly rotating gas in PG 1126−041, while in MR 2251−178 it is possibly associated with tidal debris from a recent merger or flyby. The other component is found to be extended up to a few kpc from the nuclei, and shows a high (∼800 km s−1) velocity dispersion and a blue-shifted mean velocity, as is expected from outflows driven by active galactic nuclei (AGN). We estimate mass outflow rates up to a few M⊙ yr−1 and kinetic efficiencies LKIN/LBOL between 1−4 × 10−4, in line with those of galaxies hosting AGN of similar luminosities. The momentum rates of these ionised outflows are comparable to those measured for the UFOs at sub-pc scales, which is consistent with a momentum-driven wind propagation. Pure energy-driven winds are excluded unless about 100× additional momentum is locked in massive molecular winds. In comparing the outflow properties of our sources with those of a small sample of well-studied QSOs hosting UFOs from the literature, we find that winds seem to systematically lie either in a momentum-driven or an energy-driven regime, indicating that these two theoretical models bracket the physics of AGN-driven winds very well.
Algorithmic Thresholds in Mean Field Spin Glasses

Optimizing a high-dimensional non-convex function is, in general, computationally hard and many problems of this type are hard to solve even approximately. Complexity theory characterizes the optimal approximation ratios achievable in polynomial time in the worst case. On the other hand, when the objective function is random, worst case approximation ratios are overly pessimistic. Mean field spin glasses are canonical families of random energy functions over the discrete hypercube $\{-1,+1\}^N$. The near-optima of these energy landscapes are organized according to an ultrametric tree-like structure, which enjoys a high degree of universality. Recently, a precise connection has begun to emerge between this ultrametric structure and the optimal approximation ratio achievable in polynomial time in the typical case. A new approximate message passing (AMP) algorithm has been proposed that leverages this connection. The asymptotic behavior of this algorithm has been analyzed, conditional on the nature of the solution of a certain variational problem. In this paper we describe the first implementation of this algorithm and the first numerical solution of the associated variational problem. We test our approach on two prototypical mean-field spin glasses: the Sherrington-Kirkpatrick (SK) model, and the $3$-spin Ising spin glass. We observe that the algorithm works well already at moderate sizes ($N\gtrsim 1000$) and its behavior is consistent with theoretical expectations. For the SK model it asymptotically achieves arbitrarily good approximations of the global optimum. For the $3$-spin model, it achieves a constant approximation ratio that is predicted by the theory, and it appears to beat the `threshold energy' achieved by Glauber dynamics. Finally, we observe numerically that the intermediate states generated by the algorithm have the properties of ancestor states in the ultrametric tree.
Language Generation with Multi-hop Reasoning on Commonsense Knowledge Graph

Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.
Divergence of insulin-like growth factor-I and II in the elasmobranch

Recent studies have shown that vertebrates, including teleostean fishes, amphibians, birds and mammals, contain two distinct insulin-like growth factor (IGF) genes. In contrast agnathans, represented by hagfish, apparently have only one IGF that has features characteristic of both IGF-I and IGF-II. Between these groups the elasmobranchs occupy a critical position in terms of the phylogeny of IGFs. We sought to determine if gene duplication and divergence of IGF-I and IGF-II occurred before or after divergence of elasmobranchs from other vertebrates by cloning IGF-like molecules from Squalus acanthias. Our analysis shows that Squalus fiver produces two distinct IGF-like molecules. One has greater sequence identity to, and conserved features characteristic of, known IGF-I molecules, while the other is more IGF-II like. These results suggest that the prototypical IGF molecule duplicated and diverged in an ancestor of the extant gnathostomes. Key words'. " Insulin-like growth factor; Squalus acanth&s (spiny dogfish shark)
Isotropic‐Nematic Phase Transition and Liquid Crystal Droplets

Liquid crystal droplets are of great interest from physics and applications. Rigorous mathematical analysis is challenging as the problem involves harmonic maps (or Oseen‐Frank energy minimizers in general), free interfaces, and topological defects which could be either inside the droplet or on its surface along with some intriguing boundary anchoring conditions for the orientation configurations. In this paper, through a study of the phase transition between the isotropic and nematic states of liquid crystal based on the Ericksen model, we can show, when the size of a droplet is much larger in comparison with the ratio of the Frank constants to the surface tension, a Γ‐convergence theorem for minimizers. This Γ‐limit is in fact the sharp interface limit for the phase transition between the isotropic and nematic regions when the small parameter ε, corresponding to the transition layer width, goes to 0. This limiting process not only provides a geometric description of the shape of the droplet as one would expect, and surprisingly it also gives the anchoring conditions for the orientations of liquid crystals on the surface of the droplet depending on material constants. In particular, homeotropic, tangential, and even free boundary conditions as assumed in earlier phenomenological modelings arise naturally, provided the surface tension, Frank‐Ericksen constants are in suitable ranges. © 2022 Wiley Periodicals LLC.
On the Uniqueness of Inverse Problems with Fourier-domain Measurements and Generalized TV Regularization

We study the super-resolution problem of recovering a periodic continuous-domain function from its low-frequency information. This means that we only have access to possibly corrupted versions of its Fourier samples up to a maximum cut-off frequency. The reconstruction task is specified as an optimization problem with generalized total-variation regularization involving a pseudo-differential operator. Our special emphasis is on the uniqueness of solutions. We show that, for elliptic regularization operators (e.g., the derivatives of any order), uniqueness is always guaranteed. To achieve this goal, we provide a new analysis of constrained optimization problems over Radon measures. We demonstrate that either the solutions are always made of Radon measures of constant sign, or the solution is unique. Doing so, we identify a general sufficient condition for the uniqueness of the solution of a constrained optimization problem with TV-regularization, expressed in terms of the Fourier samples.
Large magnetoresistance in the iron-free pnictide superconductor LaRu2P2

The magnetoresistance (MR) of iron pnictide superconductors is often dominated by electron–electron correlations and deviates from the H 2 or saturating behaviors expected for uncorrelated metals. Contrary to similar Fe-based pnictide systems, the superconductor LaRu2P2 (T c = 4 K) shows no enhancement of electron–electron correlations. Here we report a non-saturating MR deviating from the H 2 or saturating behaviors in LaRu2P2. We present results in single crystals of LaRu2P2, where we observe a MR following H 1.3 up to 22 T. We discuss our result by comparing the bandstructure of LaRu2P2 with that of Fe based pnictide superconductors. The different orbital structures of Fe and Ru leads to a 3D Fermi surface with negligible bandwidth renormalization in LaRu2P2, that contains a large open sheet over the whole Brillouin zone. We show that the large MR in LaRu2P2 is unrelated to the one obtained in materials with strong electron–electron correlations and that it is compatible instead with conduction due to open orbits on the rather complex Fermi surface structure of LaRu2P2.
Testing the Strong Equivalence Principle: Detection of the External Field Effect in Rotationally Supported Galaxies

The strong equivalence principle (SEP) distinguishes general relativity (GR) from other viable theories of gravity. The SEP demands that the internal dynamics of a self-gravitating system under freefall in an external gravitational field should not depend on the external field strength. We test the SEP by investigating the external field effect (EFE) in Milgromian dynamics (MOND), proposed as an alternative to dark matter in interpreting galactic kinematics. We report a detection of this EFE using galaxies from the Spitzer Photometry and Accurate Rotation Curves (SPARC) sample together with estimates of the large-scale external gravitational field from an all-sky galaxy catalog. Our detection is threefold: (1) the EFE is individually detected at 8σ to 11σ in “golden” galaxies subjected to exceptionally strong external fields, while it is not detected in exceptionally isolated galaxies, (2) the EFE is statistically detected at more than 4σ from a blind test of 153 SPARC rotating galaxies, giving a mean value of the external field consistent with an independent estimate from the galaxies’ environments, and (3) we detect a systematic downward trend in the weak gravity part of the radial acceleration relation at the right acceleration predicted by the EFE of the MOND modified gravity. Tidal effects from neighboring galaxies in the Λ cold dark matter (CDM) context are not strong enough to explain these phenomena. They are not predicted by existing ΛCDM models of galaxy formation and evolution, adding a new small-scale challenge to the ΛCDM paradigm. Our results point to a breakdown of the SEP, supporting modified gravity theories beyond GR.