Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence of a fast bar in the weakly-interacting galaxy NGC 4264 with MUSE

We present surface photometry and stellar kinematics of NGC 4264, a barred lenticular galaxy in the region of the Virgo Cluster undergoing a tidal interaction with one of its neighbours, NGC 4261. We measured the bar radius (abar = 3.2 ± 0.5 kpc) and strength (Sbar = 0.31 ± 0.04) of NGC 4264 from Sloan Digital Sky Survey imaging and its bar pattern speed (Ωbar = 71 ± 4 km s−1 kpc−1) using the Tremaine–Weinberg method with stellar-absorption integral-field spectroscopy performed with the Multi Unit Spectroscopic Explorer at the Very Large Telescope. We derived the circular velocity (Vcirc = 189 ± 10 km s−1) by correcting the stellar streaming velocity for asymmetric drift and calculated the corotation radius (Rcor = 2.8 ± 0.2 kpc) from the bar pattern speed. Finally, we estimated the bar rotation rate (Rcor/abar = 0.88 ± 0.23). We find that NGC 4264 hosts a strong and large bar extending out to the corotation radius. This means that the bar is rotating as fast as it can like nearly all the other bars measured so far even when the systematic error due to the uncertainty on the disc position angle is taken into account. The accurate measurement of the bar rotation rate allows us to infer that the formation of the bar of NGC 4264 was due to self-generated internal processes and not triggered by the ongoing interaction.
Structure learning in graphical models by covariance queries

We study the problem of recovering the structure underlying large Gaussian graphical models. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the sample covariance matrix. We present computationally efficient algorithms for structure recovery in Gaussian graphical models with low query and computational complexity. Our algorithms work in a regime of tree-like graphs and, more generally, for graphs of small treewidth. Our results demonstrate that for large classes of graphs, the structure of the corresponding Gaussian graphical models can be determined much faster than even computing the empirical covariance matrix.
In Hindsight: A Smooth Reward for Steady Exploration

In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, in an attempt to estimate the action value function of the optimal policy. Conventionally, the loss function is defined as the temporal difference between the action value and the expected (discounted) reward, however it focuses solely on the future, leading to overestimation errors. We extend the well-established Q-learning techniques by introducing the hindsight factor, an additional loss term that takes into account how the model progresses, by integrating the historic temporal difference as part of the reward. The effect of this modification is examined in a deterministic continuous-state space function estimation problem, where the overestimation phenomenon is significantly reduced and results in improved stability. The underlying effect of the hindsight factor is modeled as an adaptive learning rate, which unlike existing adaptive optimizers, takes into account the previously estimated action value. The proposed method outperforms variations of Q-learning, with an overall higher average reward and lower action values, which supports the deterministic evaluation, and proves that the hindsight factor contributes to lower overestimation errors. The mean average score of 100 episodes obtained after training for 10 million frames shows that the hindsight factor outperforms deep Q-networks, double deep Q-networks and dueling networks for a variety of ATARI games.
Probing magnetism via spin dynamics in graphene/2D-ferromagnet heterostructures

The recent discovery of two-dimensional magnetic insulators has generated a great deal of excitement over their potential for nanoscale manipulation of spin or magnetism. One intriguing use for these materials is to put them in contact with graphene, with the goal of making graphene magnetic while maintaining its unique electronic properties. Such a system could prove useful in applications such as magnetic memories, or could serve as a host for exotic states of matter. Proximity to a magnetic insulator will alter the spin transport properties of graphene, and the strength of this interaction can be probed with Hanle spin precession experiments. To aid in the analysis of such experiments, in this work we derive an explicit expression for Hanle spin precession in graphene interfaced with a ferromagnetic insulator whose magnetization points perpendicular to the graphene plane. We find that this interface results in a shifted and asymmetric Hanle response, and we discuss how this behavior can be used to interpret measurements of spin transport in these systems.
Single-Channel Speech Separation with Auxiliary Speaker Embeddings

We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural network based on residual blocks, and uses learnt speaker embeddings created from additional clean context recordings of the two speakers as input to assist in attributing the different time-frequency bins to the two speakers. In experiments, we show that the proposed model yields good performance in the source separation task, and outperforms the state-of-the-art baselines. Specifically, separating speech from the challenging VoxCeleb dataset, the proposed model yields 4.79dB signal-to-distortion ratio, 8.44dB signal-to-artifacts ratio and 7.11dB signal-to-interference ratio.
Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.
The Value of Collaboration in Convex Machine Learning with Differential Privacy

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.
Séparation de sources doublement non stationnaire

Les techniques de separation aveugle de sources (blind source separation, BSS) visent a estimer conjointement des signaux sources et une matrice de melange a partir des observations de melanges. Cet article aborde un probleme de BSS doublement non stationnaire, ou la matrice de melange depend du temps et les sources sont non stationnaires. Plus precisement, ce sont des signaux stationnaires deformes, suivant le modele de [1]. Nous presentons un algorithme joint pour la BSS et l'estimation des deformations brisant la stationnarite et des spectres. Il s'appuie sur des approximations appropriees du comportement de la transformee en ondelettes de ces signaux non stationnaires. Les performances cet algorithme sont evaluees sur des simulations numeriques et comparees a d'autres algorithmes de BSS non stationnaires.
EasyCrash: Exploring Non-Volatility of Non-Volatile Memory for High Performance Computing Under Failures

Emerging non-volatile memory (NVM) is promising for building future HPC. Leveraging the non-volatility of NVM as main memory, we can restart the application using data objects remaining on NVM when the application crashes. This paper explores this solution to handle HPC under failures, based on the observation that many HPC applications have good enough intrinsic fault tolerance. To improve the possibility of successful recomputation with correct outcomes and ignorable performance loss, we introduce EasyCrash, a framework to decide how to selectively persist application data objects during application execution. Our evaluation shows that EasyCrash transforms 54% of crashes that cannot correctly recompute into the correct computation while incurring a negligible performance overhead (1.5% on average). Using EasyCrash and application intrinsic fault tolerance, 82% of crashes can successfully recompute. When EasyCrash is used with a traditional checkpoint scheme, it enables up to 24% improvement (15% on average) in system efficiency.
Best Split Nodes for Regression Trees

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of squares error (the impurity) along a particular variable. This paper aims to study the bias and adaptive properties of regression trees constructed with CART. In doing so, we derive an interesting connection between the bias and the mean decrease in impurity (MDI) measure of variable importance---a tool widely used for model interpretability---defined as the sum of impurity reductions over all non-terminal nodes in the tree. In particular, we show that the size of a terminal subnode for a variable is small when the MDI for that variable is large and that this relationship is exponential---confirming theoretically that decision trees with CART have small bias and are adaptive to signal strength and direction. Finally, we apply these individual tree bounds to tree ensembles and show consistency of Breiman's random forests. The context is surprisingly general and applies to a wide variety of multivariable data generating distributions and regression functions. The main technical tool is an exact characterization of the conditional probability content of the daughter nodes arising from an optimal split, in terms of the partial dependence function and reduction in impurity.
Experimental Security Analysis of Controller Software in SDNs: A Review

The software defined networking paradigm relies on the programmability of the network to automatically perform management and reconfiguration tasks. The result of adopting this programmability feature is twofold: first by designing new solutions and, second, by concurrently making room for the exploitation of new security threats. As a malfunction in the controller software may lead to a collapse of the network, assessing the security of solutions before their deployment, is a major concern in SDNs. In light of this, we have conducted a comprehensive review of the literature on the experimental security analysis of the control plane in SDNs, with an emphasis on vulnerabilities of the controller software. Additionally, we have introduced a taxonomy of the techniques found in the literature with regard to the experimental security analysis of SDN controller software. Furthermore, a comparative study has been carried out of existing experimental approaches considering the security requirements defined by the Open Network Foundation (ONF). As a result, we highlighted that there is a need for a standardization of the methodologies employed for automated security analysis, that can meet the appropriate requirements, and support the development of reliable and secure software for SDNs.
Front-end CMOS chipset for fiber-based gigabit Ethernet

We present three front-end ICs implemented in 0.6 /spl mu/m CMOS process that meet or exceed the specifications for 1.25 Gb/s fiber-based gigabit Ethernet. The two-stage differential transimpedance amplifier utilizes bondwire based inductive peaking to attain 1.1 GHz bandwidth. The quantizer employs novel modified Cherry-Hooper architecture to achieve a 52 dB dynamic range and 1.5 GHz bandwidth. The laser driver features on chip automatic laser power control circuitry and provides up to 40 mA of modulation current at 1.25 Gb/s.
Cross-Channel Correlation Preserved Three-Stream Lightweight CNNs for Demosaicking

Demosaicking is a procedure to reconstruct full RGB images from Color Filter Array (CFA) samples, none of which has all color components available. Recent deep Convolutional Neural Networks (CNN) based models have obtained state of the art accuracy on benchmark datasets. However, due to the sequential feature extraction manner of CNNs, deep demosaicking models may be over slow for daily use cameras. In this paper, we decouple deep sequential demosaicking to three-stream lightweight networks, which restore the green channel, the green-red difference plane and the green-blue difference plane respectively. This strategy allows independent offline training and parallel online estimation, whilst preserving the intrinsic cross-channel correlation of natural images. Moreover, this allows designing each stream according to the various restoration difficulty of each channel. As validated by extensive experiments, our method achieves top accuracy at fast speed. Source code will be released along with paper publication.
Weak ferromagnetism in hexagonal 
Mn3Z
 alloys 
(Z=Sn,Ge,Ga)

We present combined spin model and first principles electronic structure calculations to study the weak ferromagnetism in bulk Mn$_3$Z (Z=Sn, Ge, Ga) compounds. The spin model parameters were determined from a spin-cluster expansion technique based on the relativistic disordered local moment formalism implemented in the screened Korringa--Kohn--Rostoker method. We describe the magnetic ground state of the system within a three-sublattice model and investigate the formation of the weak ferromagnetic states in terms of the relevant model parameters. First, we give a group-theoretical argument how the point-group symmetry of the lattice leads to the formation of weak ferromagnetic states. Then we study the ground states of the classical spin model and derive analytical expressions for the weak ferromagnetic distortions by recovering the main results of the group-theoretical analysis. As a third approach we obtain the weak ferromagnetic ground states from self-consistent density functional calculations and compare our results with previous first principles calculations and with available experimental data. In particular, we demonstrate that the orbital moments follow a decomposition predicted by group theory. For a deeper understanding of the formation of weak ferromagnetism we selectively trace the effect of the spin-orbit coupling at the Mn and Z sites. In addition, for the case of Mn$_3$Ga, we gain information on the role of the induced moment of Ga from constrained local density functional calculations.
Confluent-Drawing Parallel Coordinates: Web-Based Interactive Visual Analytics of Large Multi-Dimensional Data

Parallel coordinates plot is one of the most popular and widely used visualization techniques for multi-dimensional data sets. Its main challenges for large-scale data sets are visual clutter and overplotting which hamper the recognition of patterns and trends in the data. In this paper, we propose a confluent drawing approach of parallel coordinates to support the web-based interactive visual analytics of large multi-dimensional data. The proposed method maps multi-dimensional data to node-link diagrams through the data binning-based clustering for each dimension. It uses density-based confluent drawing to visualize clusters and edges to reduce visual clutter and overplotting. Its rendering time is independent of the number of data items. It supports interactive visualization of large data sets without hardware acceleration in a normal web browser. Moreover, we design interactions to control the data binning process with this approach to support interactive visual analytics of large multi-dimensional data sets. Based on the proposed approach, we implement a web-based visual analytics application. The efficiency of the proposed method is examined through experiments on several data sets. The effectiveness of the proposed method is evaluated through a user study, in which two typical tasks of parallel coordinates plot are performed by participants to compare the proposed method with another parallel coordinates bundling technique. Results show that the proposed method significantly enhances the web-based interactive visual analytics of large multi-dimensional data.
Ising chains with competing interactions in the presence of long-range couplings

In this paper we study an Ising spin chain with short-range competing interactions in the presence of long-range ferromagnetic interactions in the canonical ensemble. The simultaneous presence of the frustration induced by the short-range couplings together with their competition with the long-range term gives rise to a rich thermodynamic-phase diagram. We compare our results with the limit in which one of two local interactions is turned off, which was previously studied in the literature. Eight regions of parameters with qualitatively distinct properties are featured, with different first- and second-order phase transition lines and critical points.
Description of unitary representations of the group of infinite 𝑝-adic integer matrices

<p>We classify irreducible unitary representations of the group of all infinite matrices over a <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="p"> <mml:semantics> <mml:mi>p</mml:mi> <mml:annotation encoding="application/x-tex">p</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-adic field (<inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="p not-equals 2"> <mml:semantics> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>≠<! -- ≠ --></mml:mo> <mml:mn>2</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">p\ne 2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>) with integer elements equipped with a natural topology. Any irreducible representation passes through a group <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G upper L"> <mml:semantics> <mml:mrow> <mml:mi>G</mml:mi> <mml:mi>L</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">GL</mml:annotation> </mml:semantics> </mml:math> </inline-formula> of infinite matrices over a residue ring modulo <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="p Superscript k"> <mml:semantics> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>k</mml:mi> </mml:msup> <mml:annotation encoding="application/x-tex">p^k</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. Irreducible representations of the latter group are induced from finite-dimensional representations of certain open subgroups. </p>
An entropic Landweber method for linear ill-posed problems

The aim of this paper is to investigate the use of a Landweber-type method involving the Shannon entropy for the regularization of linear ill-posed problems. We derive a closed form solution for the iterates and analyze their convergence behaviour both in a case of reconstructing general nonnegative unknowns as well as for the sake of recovering probability distributions. Moreover, we discuss several variants of the algorithm and relations to other methods in the literature. The effectiveness of the approach is studied numerically in several examples.
Stellar Dynamos in the Transition Regime: Multiple Dynamo Modes and Antisolar Differential Rotation

Global and semi-global convective dynamo simulations of solar-like stars are known to show a transition from an antisolar (fast poles, slow equator) to solar-like (fast equator, slow poles) differential rotation (DR) for increasing rotation rate. The dynamo solutions in the latter regime can exhibit regular cyclic modes, whereas in the former one, only stationary or temporally irregular solutions have been obtained so far. In this paper we present a semi-global dynamo simulation in the transition region, exhibiting two coexisting dynamo modes, a cyclic and a stationary one, both being dynamically significant. We seek to understand how such a dynamo is driven by analyzing the large-scale flow properties (DR and meridional circulation) together with the turbulent transport coefficients obtained with the test-field method. Neither an αΩ dynamo wave nor an advection-dominated dynamo are able to explain the cycle period and the propagation direction of the mean magnetic field. Furthermore, we find that the α effect is comparable or even larger than the Ω effect in generating the toroidal magnetic field, and therefore, the dynamo seems to be of α2Ω or α2 type. We further find that the effective large-scale flows are significantly altered by turbulent pumping.
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling. As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models. We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved. Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.