Miracle-dz/UniFAR-FTUs huggingface dataset:
Supernova 1954J (Variable 12) in NGC 2403 Unmasked

We have confirmed that the precursor star of the unusual supernova 1954J (also known as Variable 12) in NGC 2403 survived what appears to have been a superoutburst, similar to the 1843 Great Eruption of η Carinae in the Galaxy. The apparent survivor has changed little in brightness and color over the last 8 years, and a Keck spectrum reveals characteristics broadly similar to those of η Car. This is further suggested by our identification of the actual outburst‐surviving star in high‐resolution images obtained with the Advanced Camera for Surveys on the Hubble Space Telescope. We reveal this “supernova impostor” as a highly luminous (M0V ≈ - 8.0 mag), very massive (Minitial≳25 M⊙) eruptive star, now surrounded by a dusty (AV ≈ 4 mag) nebula, similar to η Car’s famous Homunculus.
Matter bounce cosmology with a generalized single field: non-Gaussianity and an extended no-go theorem

We extend the matter bounce scenario to a more general theory in which the background dynamics and cosmological perturbations are generated by a k-essence scalar field with an arbitrary sound speed. When the sound speed is small, the curvature perturbation is enhanced, and the tensor-to-scalar ratio, which is excessively large in the original model, can be sufficiently suppressed to be consistent with observational bounds. Then, we study the primordial three-point correlation function generated during the matter-dominated contraction stage and find that it only depends on the sound speed parameter. Similar to the canonical case, the shape of the bispectrum is mainly dominated by a local form, though for some specific sound speed values a new shape emerges and the scaling behaviour changes. Meanwhile, a small sound speed also results in a large amplitude of non-Gaussianities, which is disfavored by current observations. As a result, it does not seem possible to suppress the tensor-to-scalar ratio without amplifying the production of non-Gaussianities beyond current observational constraints (and vice versa). This suggests an extension of the previously conjectured no-go theorem in single field nonsingular matter bounce cosmologies, which rules out a large class of models. However, the non-Gaussianity results remain as a distinguishable signature of matter bounce cosmology and have the potential to be detected by observations in the near future.
A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.
An improved Toeplitz approximation method

The authors suggest a modification of the Toeplitz approximation method for estimating frequencies of multiple sinusoids from covariance measurements. The method constructs a state-feedback matrix following a low-rank approximation of the Toeplitz covariance matrix via singular-value decomposition. Ideally, the eigenvalues of this state-feedback matrix will be on the unit circle in the complex plane, and the angles that they make with the real axis will be equal to the unknown sinusoid frequencies. The modification proposed here exploits this prior knowledge of the modulus of the eigenvalues and guarantees that even in the presence of noise the eigenvalues of the estimated state-feedback matrix will lie on the unit circle. <<ETX>>
Results of dark matter searches with the ANTARES neutrino telescope

Neutrino telescopes have a wide scientific scope. One of their main goals is the detection of dark matter, for which they have specific advantages. Neutrino telescopes offer the possibility of looking at several kinds of sources, not all of them available to other indirect searches. In this work we provide an overview of the results obtained by the ANTARES neutrino telescope, which has been taking data for almost ten years. One of the most interesting ones is the Sun, since a detection of high energy neutrinos from it would be a very clean indication of dark matter, given that no significant astrophysical backgrounds are expected, contrary to other indirect searches. Moreover, the limits from neutrino telescopes for spin-dependent cross section are the most restrictive ones. Another interesting source is the Galactic Centre, for which ANTARES has a better visibility than IceCube, due to its geographical location. This search gives limits on the annihilation cross section. Other dark matter searches carried out in ANTARES include the Earth and dwarf galaxies.
Design of permanent magnet eddy current brake for a small scaled electromagnetic launch model

A variable pole-pitch double-sided permanent magnet (PM) linear eddy current brake (LECB) is proposed for a small scaled electromagnetic launch model. A two-dimensional (2D) analytical steady state model is presented for the double-sided PM-LECB, and the expression for the braking force is derived. Based on the analytical model, the material and eddy current skin effect of the conducting plate are analyzed. Moreover, a variable pole-pitch double-sided PM-LECB is proposed for the effective braking of the moving plate. In addition, the braking force is predicted by finite element (FE) analysis, and the simulated results are in good agreement with the analytical model. Finally, a prototype is presented to test the braking profile for validation of the proposed design.
Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses

Existing approaches for disfluency detection typically require the existence of large annotated datasets. However, current datasets for this task are limited, suffer from class imbalance, and lack some types of disfluencies that can be encountered in real-world scenarios. This work proposes LARD, a method for automatically generating artificial disfluencies from fluent text. LARD can simulate all the different types of disfluencies (repetitions, replacements and restarts) based on the reparandum/interregnum annotation scheme. In addition, it incorporates contextual embeddings into the disfluency generation to produce realistic context-aware artificial disfluencies. Since the proposed method requires only fluent text, it can be used directly for training, bypassing the requirement of annotated disfluent data. Our empirical evaluation demonstrates that LARD can indeed be effectively used when no or only a few data are available. Furthermore, our detailed analysis suggests that the proposed method generates realistic disfluencies and increases the accuracy of existing disfluency detectors.
Normative Modeling on Multimodal Neuroimaging Data using Variational Autoencoders

Normative modelling is an emerging method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying how each patient deviates from the expected normative pattern that has been learned from a healthy control distribution. Existing deep learning based normative models on magnetic resonance imaging (MRI) neuroimaging data use unimodal autoencoders with a single encoder and decoder that may fail to capture the relationship between brain measurements extracted from different MRI modalities. In this work, we propose multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities and apply it for normative modeling. The deviation maps generated by our proposed multimodal model (mmVAE) are more sensitive to disease staging within AD, have a better correlation with patient cognition and higher number of brain regions with statistically signiﬁcant deviations compared to a unimodal baseline model with all modalities concatenated as a single input.
Bayesian optimization of the beam injection process into a storage ring

We have evaluated the data-efficient Bayesian optimization method for the specific task of injection tuning in a circular accelerator. In this paper, we describe the implementation of this method at the Karlsruhe Research Accelerator with up to nine tuning parameters, including the determination of the associated hyperparameters. We show that the Bayesian optimization method outperforms manual tuning and the commonly used Nelder-Mead optimization algorithm both in simulation and experiment. The algorithm was also successfully used to ease the commissioning phase after the installation of new injection magnets and is regularly used during accelerator operations. We demonstrate that the introduction of context variables that include intra-bunch scattering effects, such as the Touschek effect, further improves the control and robustness of the injection process.
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations

Neural Stochastic Differential Equations (NSDE) have been trained as both Variational Autoencoders, and as GANs. However, the resulting Stochastic Differential Equations can be hard to interpret or analyse due to the generic nature of the drift and diffusion fields. By restricting our NSDE to be of the form of Langevin dynamics, and training it as a VAE, we obtain NSDEs that lend themselves to more elaborate analysis and to a wider range of visualisation techniques than a generic NSDE. More specifically, we obtain an energy landscape, the minima of which are in one-to-one correspondence with latent states underlying the used data. This not only allows us to detect states underlying the data dynamics in an unsupervised manner, but also to infer the distribution of time spent in each state according to the learned SDE. More in general, restricting an NSDE to Langevin dynamics enables the use of a large set of tools from computational molecular dynamics for the analysis of the obtained results.
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks

Signed networks (networks with positive and negative edges) commonly arise in various domains from molecular biology to social media. The edge signs -- i.e., the graph signage -- represent the interaction pattern between the vertices and can provide insights into the underlying system formation process. Generative models considering signage formation are essential for testing hypotheses about the emergence of interactions and for creating synthetic datasets for algorithm benchmarking (especially in areas where obtaining real-world datasets is difficult). In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in the process of organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology, and, based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, novel biological hypotheses, and realistic benchmark datasets in the realm of gene regulation.
Predicting Human Mobility via Self-supervised Disentanglement Learning

Deep neural networks have recently achieved considerable improvements in learning human behavioral patterns and individual preferences from massive spatial-temporal trajectories data. However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions. In addition, the inherent sparsity and under-explored heterogeneous collaborative items pertaining to human check-ins hinder the potential exploitation of human diverse periodic regularities as well as common interests. Motivated by recent advances in disentanglement learning, in this study we propose a novel disentangled solution called SSDL for tackling the next POI prediction problem. SSDL primarily seeks to disentangle the potential time-invariant and time-varying factors into different latent spaces from massive trajectories data, providing an interpretable view to understand the intricate semantics underlying human diverse mobility representations. To address the data sparsity issue, we present two realistic trajectory augmentation approaches to enhance the understanding of both the human intrinsic periodicity and constantly-changing intents. In addition, we devise a POI-centric graph structure to explore heterogeneous collaborative signals underlying historical check-ins. Extensive experiments conducted on four real-world datasets demonstrate that our proposed SSDL significantly outperforms the state-of-the-art approaches -- for example, it yields up to 8.57% improvements on ACC@1.
Systematic errors in weak lensing: application to SDSS galaxy-galaxy weak lensing

Weak lensing is emerging as a powerful observational tool to constrain cosmological models, but is at present limited by an incomplete understanding of many sources of systematic error. Many of these errors are multiplicative and depend on the population of background galaxies. We show how the commonly cited geometric test, which is rather insensitive to cosmology, can be used as a ratio test of systematics in the lensing signal at the 1 per cent level. We apply this test to the galaxy-galaxy lensing analysis of the Sloan Digital Sky Survey (SDSS), which at present is the sample with the highest weak lensing signal-to-noise ratio and has the additional advantage of spectroscopic redshifts for lenses. This allows one to perform meaningful geometric tests of systematics for different subsamples of galaxies at different mean redshifts, such as brighter galaxies, fainter galaxies and high-redshift luminous red galaxies, both with and without photometric redshift estimates. We use overlapping objects between SDSS and the DEEP2 and 2df-Sloan LRG and Quasar (2SLAQ) spectroscopic surveys to establish accurate calibration of photometric redshifts and to determine the redshift distributions for SDSS. We use these redshift results to compute the projected surface density contrast ΔΣ around 259 609 spectroscopic galaxies in the SDSS; by measuring ΔΣ with different source samples we establish consistency of the results at the 10 per cent level (1σ). We also use the ratio test to constrain shear calibration biases and other systematics in the SDSS survey data to determine the overall galaxy-galaxy weak lensing signal calibration uncertainty. We find no evidence of any inconsistency among many subsamples of the data.
Are atomic insulators distinguishable?

Topological classiﬁcation of quantum solids often (if not always) groups all trivial atomic or normal insulators (NIs) into the same featureless family. As we argue here, this is not necessarily the case always. In particular, when the global phase diagram of electronic crystals harbors topological insulators with the band inversion at various time reversal invariant momenta K TIinv in the Brillouin zone, their proximal NIs display noninverted band gap minima at K NImin = K TIinv . In such systems, once topological superconductors nucleate from NIs, the inversion of the Bogoliubov de Gennes bands takes place at K BdGinv = K NImin , inheriting from the parent state. We showcase this (possibly general) proposal for two-dimensional time reversal symmetry breaking insulators. Then distinct quantized thermal Hall conductivity and responses to dislocation lattice defects inside the paired states (tied with K BdGinv or K NImin ), in turn unambiguously identify diﬀerent parent atomic NIs.
Rounding via Low Dimensional Embeddings

A regular graph $G = (V,E)$ is an $(\varepsilon,\gamma)$ small-set expander if for any set of vertices of fractional size at most $\varepsilon$, at least $\gamma$ of the edges that are adjacent to it go outside. In this paper, we give a unified approach to several known complexity-theoretic results on small-set expanders. In particular, we show: 1. Max-Cut: we show that if a regular graph $G = (V,E)$ is an $(\varepsilon,\gamma)$ small-set expander that contains a cut of fractional size at least $1-\delta$, then one can find in $G$ a cut of fractional size at least $1-O\left(\frac{\delta}{\varepsilon\gamma^6}\right)$ in polynomial time. 2. Improved spectral partitioning, Cheeger's inequality and the parallel repetition theorem over small-set expanders. The general form of each one of these results involves square-root loss that comes from certain rounding procedure, and we show how this can be avoided over small set expanders. Our main idea is to project a high dimensional vector solution into a low-dimensional space while roughly maintaining $\ell_2^2$ distances, and then perform a pre-processing step using low-dimensional geometry and the properties of $\ell_2^2$ distances over it. This pre-processing leverages the small-set expansion property of the graph to transform a vector valued solution to a different vector valued solution with additional structural properties, which give rise to more efficient integral-solution rounding schemes.
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\"ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.
The excitation mechanisms and evolutionary stages of UWISH2 planetary nebula candidates

We present medium-resolution K-band long-slit spectroscopy of 29 true, likely, possible, and candidate Galactic Plane planetary nebulae (PNe) from the UWISH2 survey, many of which have only been recently discovered. These objects are bright in molecular hydrogen (H2) emission, and many have bipolar morphologies. Through the detection of the Brγ emission line, which traces ionized hydrogen, we find that the majority of the candidate PNe are indeed likely to be PNe, while 2 of the targets are more likely young stellar objects (YSOs) or pre-planetary nebulae (pPNe). We detect Brγ in 13 objects which have no detection in IPHAS or SHS Hα surveys. This implies that they are potential members of the little-known optically obscured PN population, hidden from wide-field optical surveys. We use the spatial extent of the H2 1-0 S(1) and Brγ lines to estimate the evolutionary stage of our targets, and find that W-BPNe (bipolar PNe with pinched waist morphologies) are likely to be younger objects, while R-BPNe (bipolar PNe with large ring structures) are more evolved. We use line ratios to trace the excitation mechanism of the H2, and find the 1-0 S(1) / 2-1 S(1) and 1-0 S(1) / Brγ ratios are higher for R-BPNe, implying the H2 is thermally excited. However, in W-BPNe, these ratios are lower, and so UV-fluorescence may be contributing to the excitation of H2.
Can a Magnetic Field Destroy a Spin-Density-Wave Phase in a Quasi-One-Dimensional Conductor?

It is known that, in a pure one-dimensional case, Charge-Density-Wave (CDW) phase is destroyed by a magnetic field, whereas Spin-Density-Wave (SDW) one does not feel the field. In reality, SDW phase is often observed in quasi-one-dimensional (Q1D) conductors due to the so-called ”nesting” property of their electron spectra. We show that, in the latter case, a high magnetic field generates some ”anti-nesting” term in a Q1D electron spectrum, which destroys SDW phase. We suggest to perform the corresponding experiments in SDW phases of the real Q1D organic conductors with chemical formula (TMTSF)2X (X=PF6, ClO4, etc.).
Arrangement and chemical complexity in an $N$-body quantum system

We introduce and discuss the concept of arrangement , traditionally found in the context of chemical reactions and few-body rearrangement collisions, in the general context of an N -body quantum system. We show that the ability for particles to attract and bind is a key source of complexity of an N -body quantum system, and arrangement is an emergent concept necessitated by the description of this complexity. For an N -body system made of particles of which some or all can attract and bind, the concept of arrangement is not only necessary for a full characterization of its quantum states and processes, but it also provides a basis for the understanding and the description of the structure of its energy spectrum. We also discuss and formulate multiparticle separability in an N body system, the mathematical foundation that underlies the arrangement concept in an N -body theory.
Engineering Monosemanticity in Toy Models

In some neural networks, individual neurons correspond to natural ``features'' in the input. Such \emph{monosemantic} neurons are of great help in interpretability studies, as they can be cleanly understood. In this work we report preliminary attempts to engineer monosemanticity in toy models. We find that models can be made more monosemantic without increasing the loss by just changing which local minimum the training process finds. More monosemantic loss minima have moderate negative biases, and we are able to use this fact to engineer highly monosemantic models. We are able to mechanistically interpret these models, including the residual polysemantic neurons, and uncover a simple yet surprising algorithm. Finally, we find that providing models with more neurons per layer makes the models more monosemantic, albeit at increased computational cost. These findings point to a number of new questions and avenues for engineering monosemanticity, which we intend to study these in future work.