Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Graph Filters for Clustering Attributed Graphs

Many real-world systems can be represented as graphs where the different entities are presented by nodes and their interactions by edges. An important task in studying large datasets is graph clustering. While there has been a lot of work on graph clustering using the connectivity between the nodes, many real-world networks also have node attributes. Clustering attributed graphs requires joint modeling of graph structure and node attributes. Recent work has focused on graph convolutional networks and graph convolutional filters to combine structural and content information. However, these methods are mostly limited to lowpass filtering and do not explicitly optimize the filters for the clustering task. In this paper, we introduce a graph signal processing based approach, where we design polynomial graph filters optimized for clustering. The proposed approach is formulated as a two-step iterative optimization problem where graph filters that are interpretable and optimal for the given data are learned while maximizing the separation between different clusters. The proposed approach is evaluated on attributed networks and compared to the state-of-the-art graph convolutional network approaches.
Bit models of replica wormholes

: We deﬁne bit models of evaporating black holes which incorporate the eﬀects of replica wormholes. These enter as non-perturbative corrections to the gravitational inner product, arising from spacetime wormholes that connect replica black holes. The resulting models have exactly unitary evolution, predict measurements on Hawking radiation in ac-cord with a Page curve for entropy, and are compatible with a smooth horizon for infalling observers.
Binary population synthesis study of the supersoft X-ray phase of single degenerate type Ia supernova progenitors

In the single degenerate (SD) scenario for type Ia supernovae (SNe Ia), a mass-accreting white dwarf is expected to experience a supersoft X-ray source (SSS) phase. However, some recent observations showed that the expected number of mass-accreting WDs is much lower than that predicted from theory, regardless of whether they are in spiral or elliptical galaxies. In this paper, we performed a binary population synthesis study on the relative duration of the SSS phase to their whole mass-increasing phase of WDs leading to SNe Ia. We found that for about 40% of the progenitor systems, the relative duration is shorter than 2% and the evolution of the mean relative duration shows that it is always smaller than 5%, both for young and old SNe Ia. In addition, before the SNe Ia explosions, more than 55% of the progenitor systems were experiencing a dwarf novae phase and no more than 10% were staying in the SSS phase. These results are consistent with the recent observations and imply that both in early- and late-type galaxies, only a small fraction of mass-accreting WDs resulting in SNe Ia contributes to the supersoft X-ray flux. So, although our results are not directly related to the X-ray output of the SN Ia progenitor, the low supersoft X-ray luminosity observed in early type galaxies may not be able to exclude the validity of the SD model. On the contrary, it is evidence to support the SD scenario.
On the Robustness of Explanations of Deep Neural Network Models: A Survey

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.
SPRINT: A fast, new software tool for reconstructing the evolutionary past of polyploid datasets

Polyploidization is an important evolutionary process which affects organisms ranging from plants to fish and fungi. The signal left behind by it is in the form of a species' ploidy level (number of complete chromosome sets found in a cell) which is inherently non-treelike. Currently available tools for reconstructing the evolutionary past of a polyploid dataset generally start with a multi-labelled tree obtained for a dataset of interest and then derive a (phylogenetic) network from that tree in some way that reflects that past by interpreting the networks's vertices of indegree at least two as polyploidization events. Since obtaining such a tree can be computationally expensive it is paramount to have alternative approaches available that allow one to shed light into the reticulate evolutionary past of a polyploid dataset. SPRINT aims to reconstruct the evolutionary past of a polyploid dataset in terms of a binary network which realises the dataset's ploidy profile (vector of ploidy levels of the dataset's taxa) and requires the fewest number of polyploidization events. It does this by representing the ploidy level of a species x in terms of the number of directed paths from the root of the network to the leaf of the network labelled by x. SPRINT is distributed on GitHub: https://github.com/lmaher1/SPRINT.
Learning to Learn Domain-invariant Parameters for Domain Generalization

Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturing domain-invariant representations from source domains. Motivated by the insight that only partial parameters of DNNs are optimized to extract domain-invariant representations, we expect a general model that is capable of well perceiving and emphatically updating such domain-invariant parameters. In this paper, we propose two modules of Domain Decoupling and Combination (DDC) and Domain-invariance-guided Backpropagation (DIGB), which can encourage such general model to focus on the parameters that have a unified optimization direction between pairs of contrastive samples. Our extensive experiments on two benchmarks have demonstrated that our proposed method has achieved state-of-the-art performance with strong generalization capability.
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data

Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
Spanning trees in sparse expanders

Given integers $n\ge \Delta\ge 2$, let $\mathcal{T}(n, \Delta)$ be the collection of all $n$-vertex trees with maximum degree at most $\Delta$. A question of Alon, Krivelevich and Sudakov in 2007 asks for determining the best possible spectral gap condition forcing an $(n, d,\lambda)$-graph to be $\mathcal{T}(n, \Delta)$-universal, namely, it contains all members of $\mathcal{T}(n, \Delta)$ as a subgraph simultaneously. In this paper we show that for sufficiently large integer $n$ and all $\Delta\in \mathbb{N}$, every $(n, d,\lambda)$-graph with \[ \lambda\le\frac{d}{2\Delta^{5\sqrt{\log n}}} \] is $\mathcal{T}(n, \Delta)$-universal. As an immediate corollary, this implies that Alon's ingenious construction of triangle-free sparse expander is $\mathcal{T}(n, \Delta)$-universal, which provides an explicit construction of such graphs and thus solves a question of Johannsen, Krivelevich and Samotij. Our main result is formulated under a much more general context, namely, the $(n,d)$-expanders. More precisely, we show that there exist absolute constants $C,c>0$ such that the following statement holds for sufficiently large integer $n$. (1). For all $\Delta\in \mathbb{N}$, every $(n, \Delta^{5\sqrt{\log n}})$-expander is $\mathcal{T}(n, \Delta)$-universal. (2). For all $\Delta\in \mathbb{N}$ with $\Delta \le c\sqrt{n}$, every $(n, C\Delta n^{1/2})$-expander is $\mathcal{T}(n, \Delta)$-universal. Both results significantly improve a result of Johannsen, Krivelevich and Samotij, and have further implications in locally sparse expanders and Maker-Breaker games that also improve previously known results drastically.
System Safety Engineering for Social and Ethical ML Risks: A Case Study

Governments, industry, and academia have undertaken efforts to identify and mitigate harms in ML-driven systems, with a particular focus on social and ethical risks of ML components in complex sociotechnical systems. However, existing approaches are largely disjointed, ad-hoc and of unknown effectiveness. Systems safety engineering is a well established discipline with a track record of identifying and managing risks in many complex sociotechnical domains. We adopt the natural hypothesis that tools from this domain could serve to enhance risk analyses of ML in its context of use. To test this hypothesis, we apply a “best of breed” systems safety analysis, Systems Theoretic Process Analysis (STPA), to a speciﬁc high-consequence system with an important ML-driven component, namely the Prescription Drug Monitoring Programs (PDMPs) operated by many US States, several of which rely on an ML-derived risk score. We focus in particular on how this analysis can extend to identifying social and ethical risks and developing concrete design-level controls to mitigate them.
Curriculum generation using Autoencoder based continuous optimization

Research in Curriculum Learning have shown better performance on the task by optimizing the sequence of the training data. Recent works have focused on using complex reinforcement learning techniques to ﬁnd the optimal data ordering strategy to maximize learning for a given network. In this paper, we present a simple yet efﬁcient technique based on continuous optimization trained with auto-encodig procedure. We call this new approach Training Sequence Optimization (TSO). With a usual encoder-decoder setup we try to learn the latent space continuous representation of the training strategy and a predictor network is used on the continuous representation to predict the accuracy of the strategy on the ﬁxed network architecture. The performance predictor and encoder enable us to perform gradient-based optimization by gradually moving towards the latent space representation of training data ordering with potentially better accuracy. We show an empirical gain of 2AP with our generated optimal curriculum strategy over the random strategy using the CIFAR-100 and CIFAR-10 datasets and have better boosts than the existing state-of-the-art CL algorithms.
The Caffarelli–Kohn–Nirenberg inequalities for radial functions

We establish the full range of the Caffarelli-Kohn-Nirenberg inequalities for radial functions in the Sobolev and the fractional Sobolev spaces of order $0<s \le 1$. In particular, we show that the range of the parameters for radial functions is strictly larger than the one without symmetric assumption. Previous known results reveal only some special ranges of parameters even in the case $s=1$. Our proof is new and can be easily adapted to other contexts. Applications on compact embeddings are also mentioned.
Linear response theory and transient fluctuation relations for diffusion processes: a backward point of view

A formal apparatus is developed to unify derivations of the linear response theory and a variety of transient fluctuation relations for continuous diffusion processes from a backward point of view. The basis is a perturbed Kolmogorov backward equation and the path integral representation of its solution. We find that these exact transient relations could be interpreted as a consequence of a generalized Chapman–Kolmogorov equation, which intrinsically arises from the Markovian characteristic of diffusion processes.
EG ANDROMEDAE: A NEW ORBIT AND ADDITIONAL EVIDENCE FOR A PHOTOIONIZED WIND

We analyze a roughly 20 yr set of spectroscopic observations for the symbiotic binary EG And. Radial velocities derived from echelle spectra are best fit with a circular orbit having an orbital period of P = 483.3 ± 1.6 days and semi-amplitude K = 7.34 ± 0.07 km s−1. Combined with previous data, these observations rule out an elliptical orbit at the 10σ level. Equivalent widths of H i Balmer emission lines and various absorption features vary in phase with the orbital period. Relative to the radius of the red giant primary, the apparent size of the H ii region is consistent with a model where a hot secondary star with effective temperature Th ≈ 75,000 K ionizes the wind from the red giant.
ROTATING NON-KERR BLACK HOLE AND ENERGY EXTRACTION

The properties of the ergosphere and energy extraction by the Penrose process in a rotating non-Kerr black hole are investigated. It is shown that the ergosphere is sensitive to the deformation parameter ϵ and the shape of the ergosphere becomes thick as parameter ϵ increases. It is of interest to note that, compared with the Kerr black hole, the deformation parameter ϵ can enhance the maximum efficiency of the energy extraction process greatly. Especially for the case of a > M, the non-Kerr metric describes a superspinning compact object and the maximum efficiency can exceed 60%, while it is only 20.7% for the extremal Kerr black hole.
Revisiting light neutralino scenarios in the MSSM

We revisit the case of a light neutralino lightest supersymmetric particle in the framework of the minimal supersymmetric standard model. We consider a model with 11 free parameters. We show that all scenarios where the annihilation of light neutralinos rely mainly on the exchange of a light pseudoscalar are excluded by direct detection searches and by Fermi measurements of the {gamma}-flux from dwarf spheroidal galaxies. On the other hand, we find scenarios with light sleptons that satisfy all collider and astroparticle physics constraints. In this case, the lower limit on the lightest supersymmetric particle mass is 12.6 GeV. We discuss briefly how the parameter space of the model could be further probed at the LHC.
The CFHT Legacy Survey: stacked images and catalogs

This paper describes the image stacks and catalogs of the CFHT Legacy Survey produced using using the MegaPipe data pipeline at the Canadian Astronomy Data Centre. The Legacy Survey is divided into to two parts: The Deep Survey consists of 4 fields each of 1 square degree, with magnitude limits of u=27.5, g=27.9, r=27.7, i=27.4, and z=26.2, and contains 1.6 million sources. The Wide Survey consists of 171 square degree pointings split over 4 fields, with magnitude limits of u=26.0, g=26.5, r=25.9, i=25.7, and z=24.6, and contains 30 million sources. This paper describes the calibration, image stacking and catalog generation process. The images and catalogs are available on the web through several interfaces: normal image and text file catalog downloads, a Google Sky interface, an image cutout service, and a catalog database query service.
Fourier Transform of Electric Signal using Kundt's Tube

An experiment to demonstrate the Fourier transform of an electric signal using the Kundt's tube is described. The results of finding the component frequencies and an approximation to the amplitudes of two sinusoidal signals which compose an input electric signal is presented. Undergraduate students are expected to more easily relate to the meaning of a Fourier transform through such mechanical demonstrations.
Model comparison of the dark matter profiles of Fornax, Sculptor, Carina and Sextans

Aims. We compare dark matter profile models of four dwarf spheroidal galaxies satellites of the MilkyWay using Bayesian evidence. Methods. We use orbit based dynamical models to fit the 2nd and 4th moments of the line of sight velocity distributions of the Fornax, Sculptor, Carina and Sextans dwarf spheroidal galaxies. We compare NFW, Einasto and several cored profiles for their dark halos and present the probability distribution functions of the model parameters. Results. For each galaxy separately we compare the evidence for the various dark matter profiles, and find that it is not possible to distinguish between these specific parametric dark matter density profiles using the current data. Nonetheless, from the combined evidence, we find that is unlikely that all galaxies are embedded in the same type of cored profiles of the form PDM 1/(1 + r(2))(beta/2), where beta = 3, 4. For each galaxy, we also obtain an almost model independent, and therefore accurate, constraint on the logarithmic slope of the dark matter density distribution at a radius similar to r-3, i. e. where the logarithmic slope of the stellar density profile is -3. Conclusions. For each galaxy, we find that all best fit models essentially have the same mass distribution over a large range in radius (from just below r-3 to the last measured data point). This remarkable finding likely implies much stronger constraints on the characteristics that subhalos extracted from cosmological simulations should have in order to host the dSph galaxies around the Milky Way.
FIRST HIGH-RESOLUTION IMAGES OF THE SUN IN THE 2796 Å Mg ii k LINE

We present the first high-resolution solar images in the Mg ii k 2796 Å line. The images, taken through a 4.8 Å broad interference filter, were obtained during the second science flight of Sunrise in 2013 June by the Sunrise Filter Imager (SuFI) instrument. The Mg ii k images display structures that look qualitatively very similar to images taken in the core of Ca ii H. The Mg ii images exhibit reversed granulation (or shock waves) in the internetwork regions of the quiet Sun, at intensity contrasts that are similar to those found in Ca ii H. Very prominent in Mg ii are bright points, both in the quiet Sun and in plage regions, particularly near the disk center. These are much brighter than at other wavelengths sampled at similar resolution. Furthermore, Mg ii k images also show fibril structures associated with plage regions. Again, the fibrils are similar to those seen in Ca ii H images, but tend to be more pronounced, particularly in weak plage.
Pion-kaon femtoscopy in Pb-Pb collisions at 
sNN=2.76
 TeV modeled in (3+1)D hydrodynamics coupled to the Therminator 2 code, and the effect of delayed kaon emission

Non-identical particle femtoscopy measures the size of the system emitting particles ("radius") in heavy-ion collisions as well as the difference between mean emission space-time coordinates of two particle species ("emission asymmetry"). The system created in such collisions at the LHC behaves collectively and its dynamics is well described by hydrodynamic models. A significant emission asymmetry between pions and kaons, coming from collective flow, enhanced by contribution from flowing resonances is predicted. We present calculations within the (3+1)D viscous hydrodynamic model coupled to statistical hadronization code Therminator 2, corresponding to Pb--Pb collisions at $\sqrt{s_{\rm NN}}=2.76$ TeV. We obtain femtoscopic radii and emission asymmetry for pion-kaon pairs as a function of collision centrality. The radii grow linearly with cube root of particle multiplicity density. The emission asymmetry is negative and comparable to the radius, indicating that pions are emitted closer to the center of the system and/or later than kaons. Recent ALICE Collaboration measurements of identical kaon femtoscopy shows that kaons are emitted, on average, 2.1~fm/$c$ later than pions. We modify our calculation by introducing such delay and find that the system source size is only weakly affected. In contrast the pion-kaon emission asymmetry is directly sensitive to such delays and the modified calculation shows significantly lower values of asymmetry. Therefore we propose the measurement of the pion-kaon femtoscopic correlation function as a sensitive probe of the time delays in particle emission.
ROTATIONAL VELOCITIES FOR M DWARFS

We present spectroscopic rotation velocities (v sin i) for 56 M dwarf stars using high-resolution Hobby–Eberly Telescope High Resolution Spectrograph red spectroscopy. In addition, we have also determined photometric effective temperatures, masses, and metallicities ([Fe/H]) for some stars observed here and in the literature where we could acquire accurate parallax measurements and relevant photometry. We have increased the number of known v sin i values for mid M stars by around 80% and can confirm a weakly increasing rotation velocity with decreasing effective temperature. Our sample of v sin is peak at low velocities (∼3 km s−1). We find a change in the rotational velocity distribution between early M and late M stars, which is likely due to the changing field topology between partially and fully convective stars. There is also a possible further change in the rotational distribution toward the late M dwarfs where dust begins to play a role in the stellar atmospheres. We also link v sin i to age and show how it can be used to provide mid-M star age limits. When all literature velocities for M dwarfs are added to our sample, there are 198 with v sin i ⩽ 10 km s−1 and 124 in the mid-to-late M star regime (M3.0–M9.5) where measuring precision optical radial velocities is difficult. In addition, we also search the spectra for any significant Hα emission or absorption. Forty three percent were found to exhibit such emission and could represent young, active objects with high levels of radial-velocity noise. We acquired two epochs of spectra for the star GJ1253 spread by almost one month and the Hα profile changed from showing no clear signs of emission, to exhibiting a clear emission peak. Four stars in our sample appear to be low-mass binaries (GJ1080, GJ3129, Gl802, and LHS3080), with both GJ3129 and Gl802 exhibiting double Hα emission features. The tables presented here will aid any future M star planet search target selection to extract stars with low v sin i.
Massive black holes from dissipative dark matter

We show that a subdominant component of dissipative dark matter resembling the Standard Model can form many intermediate-mass black hole seeds during the first structure formation epoch. We also observe that, in the presence of this matter sector, the black holes will grow at a much faster rate with respect to the ordinary case. These facts can explain the observed abundance of supermassive black holes feeding high-redshift quasars. The scenario will have interesting observational consequences for dark substructures and gravitational wave production.