Miracle-dz/UniFAR-FTUs huggingface dataset:
44Ti ejecta in young supernova remnants

Context. Tracing unstable isotopes produced in supernova nucleosynthesis provides a direct diagnostic of supernova explosion physics. Theoretical models predict an extensive variety of scenarios, which can be constrained through observations of the abundant isotopes 56Ni and 44Ti. Direct evidence of the latter was previously found only in two core-collapse supernova events, and appears to be absent in thermonuclear supernovae. Aims. We aim to to constrain the supernova progenitor types of Cassiopeia A, SN 1987A, Vela Jr., G1.9+0.3, SN1572, and SN1604 through their 44Ti ejecta masses and explosion kinematics. Methods. We analyzed INTEGRAL/SPI observations of the candidate sources utilizing an empirically motivated high-precision background model. We analyzed the three dominant spectroscopically resolved de-excitation lines at 68, 78, and 1157 keV emitted in the decay chain of 44Ti→44Sc→44Ca. The fluxes allow the determination of the production yields of 44Ti. Remnant kinematics were obtained from the Doppler characteristics of the lines. Results. We find a significant signal for Cassiopeia A in all three lines with a combined significance of 5. 4σ. The fluxes are (3.3 ± 0.9) × 10−5 ph cm−2 s−1, and (4.2 ± 1.0) × 10−5 ph cm−2 s−1 for the 44Ti and 44Sc decay, respectively. This corresponds to a mass of (2.4 ± 0.7) × 10−4 M⊙ and (3.1 ± 0.8) × 10−4 M⊙, respectively. We obtain higher fluxes for 44Ti with our analysis of Cassiopeia A than were obtained in previous analyses. We discuss potential differences. We interpret the line width from Doppler broadening as expansion velocity of (6400 ± 1900) km s−1. We do not find any significant signal for any other candidate sources. Conclusions. We obtain a high 44Ti ejecta mass for Cassiopeia A that is in disagreement with ejecta yields from symmetric 2D models. Upper limits for the other core-collapse supernovae are in agreement with model predictions and previous studies. The upper limits we find for the three thermonuclear supernovae (G1.9+0.3, SN1572 and SN1604) consistently exclude the double detonation and pure helium deflagration models as progenitors.
Simple curl-curl-conforming finite elements in two dimensions

We construct smooth finite element de Rham complexes in two space dimensions. This leads to three families of curl-curl conforming finite elements, two of which contain two existing families. The simplest triangular and rectangular finite elements have only 6 and 8 degrees of freedom, respectively. Numerical experiments for each family demonstrate the convergence and efficiency of the elements for solving the quad-curl problem.
Observation of modulational instability and Townes soliton formation in two-dimensional Bose gases.

We experimentally study the non-equilibrium dynamics of two-dimensional atomic Bose gases quenched from repulsive to attractive interactions. We observe the manifestation of modulational instability that, instead of causing collapse, fragments a large two-dimensional superfluid into multiple wave packets universally around a threshold atom number necessary for the formation of Townes solitons. We confirm that the density distributions of quench-induced solitary waves are in excellent agreement with the stationary Townes profiles. Furthermore, our density measurements in space and time domain reveal detailed information about this dynamical process, from the hyperbolic growth of density waves, the formation of solitons, to the subsequent collision and collapse dynamics, demonstrating multiple universal behaviors in an attractive many-body system in association with the formation of a quasi-stationary state.
An FPT-Algorithm for Recognizing k-Apices of Minor-Closed Graph Classes

Let ${\cal G}$ be a graph class. We say that a graph $G$ is a $k$-apex of ${\cal G}$ if $G$ contains a set $S$ of at most $k$ vertices such that $G\setminus S$ belongs to ${\cal G}$. We prove that if ${\cal G}$ is minor-closed, then there is an algorithm that either returns a set $S$ certifying that $G$ is a $k$-apex of ${\cal G}$ or reports that such a set does not exist, in $2^{{\sf poly}(k)}\cdot n^3$ time. Here ${\sf poly}$ is a polynomial function whose degree depends on the maximum size of a minor-obstruction of ${\cal G}$, i.e., the minor-minimal set of graphs not belonging to ${\cal G}$. In the special case where ${\cal G}$ excludes some apex graph as a minor, we give an alternative algorithm running in $2^{{\sf poly}(k)}\cdot n^2$ time.
DAN-SNR

Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.
Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions

The aim of sequential change-point detection is to issue an alarm when it is thought that certain probabilistic properties of the monitored observations have changed. This work is concerned with nonparametric, closed-end testing procedures based on differences of empirical distribution functions that are designed to be particularly sensitive to changes in the comtemporary distribution of multivariate time series. The proposed detectors are adaptations of statistics used in a posteriori (offline) change-point testing and involve a weighting allowing to give more importance to recent observations. The resulting sequential change-point detection procedures are carried out by comparing the detectors to threshold functions estimated through resampling such that the probability of false alarm remains approximately constant over the monitoring period. A generic result on the asymptotic validity of such a way of estimating a threshold function is stated. As a corollary, the asymptotic validity of the studied sequential tests based on empirical distribution functions is proven when these are carried out using a dependent multiplier bootstrap for multivariate time series. Large-scale Monte Carlo experiments demonstrate the good finite-sample properties of the resulting procedures. The application of the derived sequential tests is illustrated on financial data.
Symmetry restoration and the gluon mass in the Landau gauge

We investigate the generation of a gluon screening mass in Yang-Mills theory in the Landau gauge. We propose a gauge-fixing procedure where the Gribov ambiguity is overcome by summing over all Gribov copies with some weight function. This can be formulated in terms of a local field theory involving constrained, nonlinear sigma model fields. We show that a phenomenon of radiative symmetry restoration occurs in this theory, similar to what happens in the standard nonlinear sigma model in two dimensions. This results in a nonzero gluon screening mass, as seen in lattice simulations.
Is GRB 110715A the Progenitor of FRB 171209?

The physical origin of fast radio bursts (FRBs) is unknown. Young magnetars born from gamma-ray bursts (GRBs) have been suggested as a possible central engine of FRBs. We test such a hypothesis by systematically searching for GRB–FRB spatial associations from 110 FRBs and 1440 GRBs. We find that one FRB event discovered by the Parkes telescope, FRB 171209, is spatially coincident with a historical long-duration GRB 110715A at z = 0.82. The afterglow of GRB 110715A is consistent with being powered by a millisecond magnetar. The extragalactic dispersion measure of FRB 171209 is in excess of that contributed by the intergalactic medium, which can be interpreted as being contributed by a young supernova remnant associated with the GRB. Overall, the significance of the association is (2.28–2.55)σ. If the association is indeed physical, our result suggests that the magnetars associated with long GRBs can be the progenitors of at least some FRBs.
Motivic virtual signed Euler characteristics and applications to Vafa-Witten invariants.

For any scheme $M$ with a perfect obstruction theory, Jiang and Thomas associate a scheme $N$ with symmetric perfect obstruction theory. The scheme $N$ is a cone over $M$ given by the dual of the obstruction sheaf of $M$, and contains $M$ as its zero section. Locally $N$ is the critical locus of a regular function. In this note we prove that $N$ is a $d$-critical scheme in the sense of Joyce. By assuming an orientation on $N$ there exists a global motive for $N$ locally given by the motive of vanishing cycles of the local regular function. We prove a motivic localization formula under the good and circle compact $\mathbb{C}^*$-action for $N$. When taking Euler characteristic the weighted Euler characteristic of $N$ weighted by the Behrend function is the signed Euler characteristic of $M$ by motivic method. As applications we calculate the motivic generating series of the motivic Vafa-Witten invariants for K3 surfaces. This motivic series gives the result of the $\chi_y$-genus for Vafa-Witten invariants of K3 surfaces, which is the same (at instanton branch) as the K-theoretical Vafa-Witten invariants of Thomas.
Ab initio
 multishell valence-space Hamiltonians and the island of inversion

In the shell-model framework, valence-space Hamiltonians connecting multiple major-oscillator shells are of key interest for investigating the physics of neutron-rich nuclei, which have been the subject of intense experimental activity for decades. Here we present an extension of the ab initio valence-space in-medium similarity renormalization group which allows the derivation of such Hamiltonians nonperturbatively. Starting from initial two- and three-nucleon forces from chiral effective field theory, we then calculate properties of nuclei in the important island-of-inversion region above oxygen, so far unexplored with ab initio methods. Our results in the neon and magnesium isotopes indicate the importance of neutron excitation from the $sd$ to $pf$ shells and ground states dominated by intruder configurations around $N=20$, consistent with the conclusions from phenomenological studies. We also benchmark the excitation spectrum of $^{16}$O with coupled-cluster theory, finding generally good agreement, and discuss implications for ground state energies and charge radii in oxygen and calcium isotopes. Finally we outline the proper procedure for treating the long-standing issue of center-of-mass contamination, and show that with a particular choice of valence space, these spurious states can be removed successfully.
A Rigourous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?

Fine-tuning pretrained model has achieved promising performance on standard NER benchmarks. Generally, these benchmarks are blessed with strong name regularity, high mention coverage and sufficient context diversity. Unfortunately, when scaling NER to open situations, these advantages may no longer exist, and therefore raise the critical question of whether pretrained supervised models can still work well when facing these issues. As there is no currently available dataset to investigate this problem, this paper proposes to conduct randomization test on standard benchmarks. Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models. Moreover, we also construct a new open NER dataset that focuses on entity types with weak name regularity such as book, song, and movie. From both randomization test and empirical experiments, we draw the conclusions that 1) name regularity is vital for generalization to unseen mentions; 2) high mention coverage may undermine the model generalization ability and 3) context patterns may not require enormous data to capture when using pretrained supervised models.
A Complete Proof System for 1-Free Regular Expressions Modulo Bisimilarity

Robin Milner (1984) gave a sound proof system for bisimilarity of regular expressions interpreted as processes: Basic Process Algebra with unary Kleene star iteration, deadlock 0, successful termination 1, and a fixed-point rule. He asked whether this system is complete. Despite intensive research over the last 35 years, the problem is still open. This paper gives a partial positive answer to Milner's problem. We prove that the adaptation of Milner's system over the subclass of regular expressions that arises by dropping the constant 1, and by changing to binary Kleene star iteration is complete. The crucial tool we use is a graph structure property that guarantees expressibility of a process graph by a regular expression, and that is preserved when going over from a process graph to its bisimulation collapse.
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms

The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error of a learning algorithm in terms of the mutual information between the algorithm's output and the training sample. In this work, we study the proposal, by Steinke and Zakynthinou (2020), to reason about the generalization error of a learning algorithm by introducing a super sample that contains the training sample as a random subset and computing mutual information conditional on the super sample. We first show that these new bounds based on the conditional mutual information are tighter than those based on the unconditional mutual information. We then introduce yet tighter bounds, building on the "individual sample" idea of Bu, S. Zou, and Veeravalli (2019) and the "data dependent" ideas of Negrea et al. (2019), using disintegrated mutual information. Finally, we apply these bounds to the study of Langevin dynamics algorithm, showing that conditioning on the super sample allows us to exploit information in the optimization trajectory to obtain tighter bounds based on hypothesis tests.
Radiative equilibrium estimates of dust temperature and mass in high-redshift galaxies

Estimating the temperature and mass of dust in high-$z$ galaxies is essential for discussions of the origin of dust in the early Universe. However, this suffers from limited sampling of the infrared spectral-energy distribution. Here we present an algorithm for deriving the temperature and mass of dust in a galaxy, assuming dust to be in radiative equilibrium. We formulate the algorithm for three geometries: a thin spherical shell, a homogeneous sphere, and a clumpy sphere. We also discuss effects of the mass absorption coefficients of dust at ultraviolet and infrared wavelengths, $\kappa_{\rm UV}$ and $\kappa_{\rm IR}$, respectively. As an example, we apply the algorithm to a normal, dusty star-forming galaxy at $z=7.5$, A1689zD1, for which three data points in the dust continuum are available. Using $\kappa_{\rm UV}=5.0\times10^4$ cm$^2$ g$^{-1}$ and $\kappa_{\rm IR}=30(\lambda/100\mu m)^{-\beta}$ cm$^2$ g$^{-1}$ with $\beta=2.0$, we obtain dust temperatures of 38--70~K and masses of $10^{6.5-7.3}$ M$_\odot$ for the three geometries considered. We obtain similar temperatures and masses from just a single data point in the dust continuum, suggesting the usefulness of the algorithm for high-$z$ galaxies with limited infrared observations. In the clumpy-sphere case, the temperature becomes equal to that of the usual modified black-body fit, because an additional parameter describing the clumpiness works as an adjuster. The best-fit clumpiness parameter is $\xi_{\rm cl}=0.1$, corresponding to $\sim10$\% of the volume filling factor of the clumps in this high-$z$ galaxy if the clump size is $\sim10$ pc, similar to that of giant molecular clouds in the local Universe.
Dual Learning for Semi-Supervised Natural Language Understanding

Natural language understanding (NLU) converts sentences into structured semantic forms. The paucity of annotated training samples is still a fundamental challenge of NLU. To solve this data sparsity problem, previous work based on semi-supervised learning mainly focuses on exploiting unlabeled sentences. In this work, we introduce a dual task of NLU, semantic-to-sentence generation (SSG), and propose a new framework for semi-supervised NLU with the corresponding dual model. The framework is composed of dual pseudo-labeling and dual learning method, which enables an NLU model to make full use of data (labeled and unlabeled) through a closed-loop of the primal and dual tasks. By incorporating the dual task, the framework can exploit pure semantic forms as well as unlabeled sentences, and further improve the NLU and SSG models iteratively in the closed-loop. The proposed approaches are evaluated on two public datasets (ATIS and SNIPS). Experiments in the semi-supervised setting show that our methods can outperform various baselines significantly, and extensive ablation studies are conducted to verify the effectiveness of our framework. Finally, our method can also achieve the state-of-the-art performance on the two datasets in the supervised setting.
Maximum Density Divergence for Domain Adaptation

Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence (“match” in ATM) and maximizes the intra-class density (“tight” in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.
A Novel Multimodal Approach for Hybrid Brain–Computer Interface

Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-computer interface system: linear fusion, tensor fusion, and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>th-order polynomial fusion. Firstly, our results prove that the hybrid BCI system is more accurate, as expected. Secondly, the <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>th-order polynomial fusion has the best classification results out of the three methods, and also shows improvements compared with previous studies. For a motion imagery task and a mental arithmetic task, the best detection accuracy in previous papers were 74.20% and 88.1%, whereas our accuracy achieved was 77.53% and 90.19%. Furthermore, unlike complex artificial neural network methods, our proposed methods are not as computationally demanding.
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots

The deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation’s mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an in vitro dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.
Dynamic iteration schemes and port-Hamiltonian formulation in coupled DAE circuit simulation

Electric circuits are usually described by charge- and flux-oriented modified nodal analysis. In this paper, we derive models as port-Hamiltonian systems on several levels: overall systems, multiply coupled systems and systems within dynamic iteration procedures. To this end, we introduce new classes of port-Hamiltonian differential-algebraic equations. Thereby, we additionally allow for nonlinear dissipation on a subspace of the state space. Both, each subsystem and the overall system, possess a port-Hamiltonian structure. A structural analysis is performed for the new setups. Dynamic iteration schemes are investigated and we show that the Jacobi approach as well as an adapted Gauss-Seidel approach lead to port-Hamiltonian differential-algebraic equations.