Miracle-dz/UniFAR-FTUs huggingface dataset:
Two Candidate Obscured Tidal Disruption Events Coincident with High-energy Neutrinos

Recently, three optical tidal disruption event (TDE) candidates discovered by the Zwicky Transient Facility (ZTF) have been suggested to be coincident with high-energy neutrinos. They all exhibit unusually strong dust infrared echoes, with their peak times matching the neutrino arrival time even better than the optical peaks. We hereby report on two new TDE candidates that are spatially and temporally coincident with neutrinos by matching our sample of mid-infrared outbursts in nearby galaxies (MIRONG) with Gold alerts of IceCube high-energy neutrino events up to 2022 June. The two candidates show negligible optical variability according to their ZTF light curves and can therefore be classified as part of the growing population of obscured TDE candidates. The chance probability of finding two such candidates is about ∼3% by redistributing the MIRONG sources randomly in the Sloan Digital Sky Survey footprint, which will be as low as ∼0.1% (or ∼0.2%) if we limit to sources with increased fluxes (or variability amplitudes) comparable with the two matched sources. Our findings further support the potential connection between high-energy neutrinos and TDEs in dusty environments by increasing the total number of neutrino-associated TDE and TDE candidates to five, although the underlying physics remains poorly understood.
Pupil Learning Mechanism

Studies on artificial neural networks rarely address both vanishing gradients and overfitting issues. In this study, we follow the pupil learning procedure, which has the features of interpreting, picking, understanding, cramming, and organizing, to derive the pupil learning mechanism (PLM) by which to modify the network structure and weights of 2-layer neural networks (2LNNs). The PLM consists of modules for sequential learning, adaptive learning, perfect learning, and less-overfitted learning. Based upon a copper price forecasting dataset, we conduct an experiment to validate the PLM module design modules, and an experiment to evaluate the performance of PLM. The empirical results indeed approve the PLM module design and show the superiority of the proposed PLM model over the linear regression model and the conventional backpropagation-based 2LNN model.
Towards Practical Robustness Auditing for Linear Regression

We investigate practical algorithms to find or disprove the existence of small subsets of a dataset which, when removed, reverse the sign of a coefficient in an ordinary least squares regression involving that dataset. We empirically study the performance of well-established algorithmic techniques for this task -- mixed integer quadratically constrained optimization for general linear regression problems and exact greedy methods for special cases. We show that these methods largely outperform the state of the art and provide a useful robustness check for regression problems in a few dimensions. However, significant computational bottlenecks remain, especially for the important task of disproving the existence of such small sets of influential samples for regression problems of dimension $3$ or greater. We make some headway on this challenge via a spectral algorithm using ideas drawn from recent innovations in algorithmic robust statistics. We summarize the limitations of known techniques in several challenge datasets to encourage further algorithmic innovation.
Discrete fourth-order Sturm–Liouville problems

A discrete fourth-order elliptic theory on a one-dimensional interval is constructed. It is based on ‘Hermitian derivatives’ and compact higher-order finite difference operators, and is shown to possess the analogues of the standard elliptic theory such as coercivity and compactness. The discrete version of the fourthorder Sturm–Liouville problem ( d dx )4 u + d dx ( A(x) d dx u )+ B(x)u = f on a real interval is studied in terms of the functional calculus. The resulting (compact) finite difference scheme constitutes a scale of finitedimensional Sturm–Liouville problems. A major difficulty is the presence of boundaries, in contrast to periodic problems (and analogous to boundary layers in Navier–Stokes simulations). Convergence of the finite-dimensional solutions to the continuous one is proved in the general case, and optimal (O(h4)) convergence rates are obtained in the constant coefficient case. Numerical examples are given, demonstrating the optimal rate even in highly oscillatory cases.
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization Problems

We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions. The objective is composed of two functionals: one is assumed to possess the variational representation and the other is expressed in terms of the expectation operator of a possibly nonsmooth convex regularizer function. Such a regularized distributional optimization problem widely appears in machine learning and statistics, such as proximal Monte-Carlo sampling, Bayesian inference and generative modeling, for regularized estimation and generation. We propose a novel method, dubbed as Moreau-Yoshida Variational Transport (MYVT), for solving the regularized distributional optimization problem. First, as the name suggests, our method employs the Moreau-Yoshida envelope for a smooth approximation of the nonsmooth function in the objective. Second, we reformulate the approximate problem as a concave-convex saddle point problem by leveraging the variational representation, and then develope an efficient primal-dual algorithm to approximate the saddle point. Furthermore, we provide theoretical analyses and report experimental results to demonstrate the effectiveness of the proposed method.
Moment indeterminateness: the Marcel Riesz variational principle

The discrete data encoded in the power moments of a positive measure, fast decaying at infinity on euclidean space, is incomplete for recovery, leading to the concept of moment indeterminateness. On the other hand, classical integral transforms (Fourier-Laplace, Fantappi\`e, Poisson) of such measures are complete, often invertible via an effective inverse operation. The gap between the two non-uniqueness/ uniqueness phenomena is manifest in the dual picture, when trying to extend the measure, regarded as a positive linear functional, from the polynomial algebra to the full space of continuous functions. This point of view was advocated by Marcel Riesz a century ago, in the single real variable setting. Notable advances in functional analysis have root in Riesz' celebrated four notes devoted to the moment problem. A key technical ingredient being there the monotone approximation by polynomials of kernels of integral transforms. With inherent new obstacles we reappraise in the context of several real variables M. Riesz' variational principle. The result is an array of necessary and sufficient moment indeterminateness criteria, some raising real algebra questions, others involving intriguing analytic problems, all gravitating around the concept of moment separating function.
A Local Measurement Based Comprehensive Protection Scheme for AC Microgrid

The popularity of low-voltage ac distribution networks is increasing day by day. However, an efficient protection scheme for low-voltage ac distribution systems is still challenging. This paper introduces a protection scheme suitable for low-voltage grid connected and islanded ac microgrid based on local measurements in order to locate, identify and isolate faults. Here current decomposition method is specifically incorporated to accomplish fault type identification. MATLAB/SIMULINK platform is chosen to examine the performance of the proposed scheme both in grid connected and islanded low-voltage ac microgrid. The feasibility of the protection scheme is extensively investigated by simulating all types of faults with substantial variations like different fault location, different fault resistances etc. The test results ensure that the proposed protection scheme is sufficiently reliable and faster for providing complete protection in low-voltage ac microgrid.
On Updating Static Output Feedback Controllers Under State-Space Perturbation

In this paper, we propose a novel update of a nominal stabilizing static output feedback (SOF) controller for a perturbed linear system. In almost every classical feedback controller design problem, a stabilizing feedback controller is designed given a stabilizable unstable system. In realistic scenarios, the system model is usually imperfect and subject to perturbations. A typical approach to attenuate the impacts of such perturbations on the system stability is repeating the whole controller design procedure to find an updated stabilizing SOF controller. Such an approach can be inefficient and occasionally infeasible. Using the notion of minimum destabilizing real perturbation (MDRP), we construct a simple norm minimization problem (a least-squares problem) to propose an efficient update of a nominal stabilizing SOF controller that can be applied to various control engineering applications in the case of perturbed scenarios like abrupt changes or inaccurate system models. In particular, considering norm-bounded known or unknown perturbations, this paper presents updated stabilizing SOF controllers and derives sufficient stability conditions. Geometric metrics to quantitatively measure the approach's robustness are defined. Moreover, we characterize the corresponding guaranteed stability regions and specifically, for the case of norm-bounded unknown perturbations, we propose non-fragility-based robust updated stabilizing SOF controllers. Through extensive numerical simulations, we assess the effectiveness of the theoretical results.
Paléoclimats holocènes du Sahara septentrional. Dépôts lacustres et terrasses alluviales en bordure du Grand Erg Oriental à l'extrême-Sud de la Tunisie

Des paleolacs a Cerastoderma glaucum et des terrasses alluviales observees a la bordure est du Grand Erg Oriental sont dates entre c. 8 300 et 5 500 ans B.P. Cette decouverte apporte un nouvel exemple de la presence de cardiums dans les eaux continentales sahariennes et confirme l'extension de la phase humide holocene sur tout le sud du Maghreb.
Ribbonness on Classical Link

It is shown that if a link in 3-space bounds a proper oriented surface (without closed component) in the upper half 4-space, then the link bounds a proper oriented ribbon surface in the upper half 4-space which is a renewal embedding of the original surface. In particular, every slice knot is a ribbon knot, answering an old question by R. H. Fox affirmatively
Audio-visual video-to-speech synthesis with synthesized input audio

Video-to-speech synthesis involves reconstructing the speech signal of a speaker from a silent video. The implicit assumption of this task is that the sound signal is either missing or contains a high amount of noise/corruption such that it is not useful for processing. Previous works in the literature either use video inputs only or employ both video and audio inputs during training, and discard the input audio pathway during inference. In this work we investigate the effect of using video and audio inputs for video-to-speech synthesis during both training and inference. In particular, we use pre-trained video-to-speech models to synthesize the missing speech signals and then train an audio-visual-to-speech synthesis model, using both the silent video and the synthesized speech as inputs, to predict the final reconstructed speech. Our experiments demonstrate that this approach is successful with both raw waveforms and mel spectrograms as target outputs.
z-GAL: A NOEMA spectroscopic redshift survey of bright Herschel galaxies. II. Dust properties

(Abridged) We present the dust properties of 125 bright Herschel galaxies selected from the z-GAL survey. The large instantaneous bandwidth of NOEMA provides an exquisite sampling of the underlying dust continuum emission at 2 and 3 mm in the observed frame, with flux densities in at least four side bands for each source. Together with the available Herschel 250, 350, and 500 micron and SCUBA-2 850 micron flux densities, the spectral energy distribution of each source can be analyzed from the far-infrared to the millimeter, with a fine sampling of the Rayleigh-Jeans tail. This wealth of data provides a solid basis to derive robust dust properties, in particular the dust emissivity index, beta, and the dust temperature, T(dust). In order to demonstrate our ability to constrain the dust properties, we used a flux-generated mock catalog and analyzed the results under the assumption of an optically thin and optically thick modified black body emission. For the z-GAL sources, we report a range of dust emissivities with beta ~ 1.5 - 3 estimated up to high precision with relative uncertainties that vary in the range 7% - 15%, and an average of 2.2 +/- 0.3. We find dust temperatures varying from 20 to 50 K with an average of T(dust) ~ 30 K for the optically thin case and ~38 K in the optically thick case. For all the sources, we estimate the dust masses and apparent infrared luminosities (based on the optically thin approach). An inverse correlation is found between T(dust) and beta, which is similar to what is seen in the local Universe. Finally, we report an increasing trend in the dust temperature as a function of redshift at a rate of 6.5 +/- 0.5 K/z for this 500 micron-selected sample. Based on this study, future prospects are outlined to further explore the evolution of dust temperature across cosmic time.
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects

We present RGB-D-Fusion, a multi-modal conditional denoising diffusion probabilistic model to generate high resolution depth maps from low-resolution monocular RGB images of humanoid subjects. Accurately representing the human body in 3D is a very active research field given its wide variety of applications. Most 3D reconstruction algorithms rely on depth maps, either coming from low-resolution consumer-level depth sensors, or from monocular depth estimation from standard images. While many modern frameworks use VAEs or GANs for monocular depth estimation, we leverage recent advances in the field of diffusion denoising probabilistic models. We implement a multi-stage conditional diffusion model that first generates a low-resolution depth map conditioned on an image and then upsamples the depth map conditioned on a low-resolution RGB-D image. We further introduce a novel augmentation technique, depth noise augmentation, to increase the robustness of our super-resolution model. Lastly, we show how our method performs on a wide variety of humans with different body types, clothing and poses.
Classifying multilingual party manifestos: Domain transfer across country, time, and genre

Annotating costs of large corpora are still one of the main bottlenecks in empirical social science research. On the one hand, making use of the capabilities of domain transfer allows re-using annotated data sets and trained models. On the other hand, it is not clear how well domain transfer works and how reliable the results are for transfer across different dimensions. We explore the potential of domain transfer across geographical locations, languages, time, and genre in a large-scale database of political manifestos. First, we show the strong within-domain classification performance of fine-tuned transformer models. Second, we vary the genre of the test set across the aforementioned dimensions to test for the fine-tuned models' robustness and transferability. For switching genres, we use an external corpus of transcribed speeches from New Zealand politicians while for the other three dimensions, custom splits of the Manifesto database are used. While BERT achieves the best scores in the initial experiments across modalities, DistilBERT proves to be competitive at a lower computational expense and is thus used for further experiments across time and country. The results of the additional analysis show that (Distil)BERT can be applied to future data with similar performance. Moreover, we observe (partly) notable differences between the political manifestos of different countries of origin, even if these countries share a language or a cultural background.
Bifurcation analysis of a conceptual model for the Atlantic Meridional Overturning Circulation

The Atlantic Meridional Overturning Circulation (AMOC) distributes heat and salt into the Northern Hemisphere via a warm surface current toward the subpolar North Atlantic, where water sinks and returns southwards as a deep cold current. There is substantial evidence that the AMOC has slowed down over the last century. We introduce a conceptual box model for the evolution of salinity and temperature on the surface of the North Atlantic Ocean, subject to the influx of meltwater from the Greenland ice sheets. Our model, which extends a model due to Welander, describes the interaction between a surface box and a deep-water box of constant temperature and salinity, which may be convective or non-convective, depending on the density difference. Its two main parameters $\mu$ and $\eta$ describe the influx of freshwater and the threshold density between the two boxes, respectively. We use bifurcation theory to analyse two cases of the model: instantaneous switching between convective or non-convective interaction, where the system is piecewise-smooth (PWS), and the full smooth model with more gradual switching. For the PWS model we derive analytical expressions for all bifurcations. The resulting bifurcation diagram in the $(\mu,\eta)$-plane identifies all regions of possible dynamics, which we show as phase portraits - both at typical parameter points, as well as at the different transitions between them. We also present the bifurcation diagram for the case of smooth switching and show how it arises from that of the PWS case. In this way, we determine exactly where one finds bistability and self-sustained oscillations of the AMOC in both versions of the model. In particular, our results show that oscillations between temperature and salinity on the surface North Atlantic Ocean disappear completely when the transition between the convective and non-convective regimes is too slow.
Overview and Validation of the Asteroseismic Modeling Portal v2.0

The launch of NASA’s Kepler space telescope in 2009 revolutionized the quality and quantity of observational data available for asteroseismic analysis. While Kepler was able to detect solar-like oscillations in hundreds of main-sequence and subgiant stars, the Transiting Exoplanet Survey Satellite is now making similar observations for thousands of the brightest stars in the sky. The Asteroseismic Modeling Portal (AMP) is an automated and objective stellar model-fitting pipeline for asteroseismic data, which was originally developed to use models from the Aarhus Stellar Evolution Code. We briefly summarize an updated version of the AMP pipeline that uses Modules for Experiments in Stellar Astrophysics, and we present initial modeling results for the Sun and several solar analogs to validate the precision and accuracy of the inferred stellar properties.
Resolving the Type Ia supernova extinction puzzle: dust location found

The colour evolution of reddened Type Ia supernovae can place strong constraints on the location of dust and help address the question of whether the observed extinction stems from the interstellar medium or from circumstellar material surrounding the progenitor. Here we analyse BV photometry of 48 reddened Type Ia supernovae from the literature and estimate the dust location from their $B-V$ colour evolution. We find a time-variable colour excess $E(B-V)$ for 15 supernovae in our sample and constrain dust to distances between 0.013 and 45 pc ($4\times10^{16}-10^{20}$ cm). For the remaining supernovae, we obtain a constant $E(B-V)$ evolution and place lower limits on the dust distance from the explosion. In all the 48 supernovae, the inferred dust location is compatible with an interstellar origin for the extinction. This is corroborated by the observation that supernovae with relatively nearby dust ($\lesssim$ 1 pc) are located close to the center of their host galaxy, in high-density dusty regions where interactions between the supernova radiation and interstellar clouds close by are likely to occur. For supernovae showing time-variable $E(B-V)$, we identify a potential preference for low $R_\mathrm{V}$ values, unusually strong sodium absorption and blue-shifted and time-variable absorption features. Within the interstellar framework, this brings evidence to a proposed scenario where cloud-cloud collisions induced by the supernova radiation pressure can shift the grain size distribution to smaller values and enhance the abundance of sodium in the gaseous phase.
Strategies for targeting chondrosarcomas in vivo and molecular dissection of oncogenic events in chondrosarcomas: is epigenetics the culprit?

It is obvious that both epigenetic and non-epigenetic actors contribute to tumorigenesis in chondrosarcomas and more generally in other cancers. Thus, the main altered pathways in chondrosarcomas are now well established and include both epigenetic and non-epigenetic pathways such as the PI3K-AKT signaling, EGFR overexpression, SPARC overexpression, c-myc overexpression, IHH/GLI1 axis, loss of Rb function, HIF1-alpha stabilization, IDH1 mutations, hypermethylation and SIRT1. This review aims to provide a detailed analysis of these pathways and highlights recurrent interactions between non-epigenetic and epigenetic actors in chondrosarcomas, raising the intriguing possibility of developing therapeutics targeting both epigenetic and non-epigenetic actors and supporting data from previous studies. Finally, we propose some strategies for targeting chondrosarcomas in vivo based on properties of this tumor.
EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction

Social platforms have emerged as crucial platforms for disseminating information and discussing real-life social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. However, most existing approaches only exploit keyword burstiness or network structures to detect unspecified events. Thus, they often need help identifying unknown events regarding the challenging nature of events and social data. Social data, e.g., tweets, is characterized by misspellings, incompleteness, word sense ambiguation, irregular language, and variation in aspects of opinions. Moreover, extracting discriminative features and patterns for evolving events by exploiting the limited structural knowledge is almost infeasible. To address these challenges, in this paper, we propose a novel framework, namely EnrichEvent, that leverages the linguistic and contextual representations of streaming social data. In particular, we leverage contextual and linguistic knowledge to detect semantically related tweets and enhance the effectiveness of the event detection approaches. Eventually, our proposed framework produces cluster chains for each event to show the evolving variation of the event through time. We conducted extensive experiments to evaluate our framework, validating its high performance and effectiveness in detecting and distinguishing unspecified social events.