Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing the spectral shape of dust emission with the DustPedia galaxy sample

The objective of this paper is to understand the variance of the far-infrared (FIR) spectral energy distribution (SED) of the DustPedia galaxies, and its link with the stellar and dust properties. An interesting aspect of the dust emission is the inferred FIR colours which could inform us about the dust content of galaxies, and how it varies with the physical conditions within galaxies. However, the inherent complexity of dust grains as well as the variety of physical properties depending on dust, hinder our ability to utilize their maximum potential. We use principal component analysis (PCA) to explore new hidden correlations with many relevant physical properties such as the dust luminosity, dust temperature, dust mass, bolometric luminosity, star formation rate (SFR), stellar mass, specific SFR, dust-to-stellar mass ratio, the fraction of absorbed stellar luminosity by dust (fabs), and metallicity. We find that 95 per cent of the variance in our sample can be described by two principal components (PCs). The first component controls the wavelength of the peak of the SED, while the second characterizes the width. The physical quantities that correlate better with the coefficients of the first two PCs, and thus control the shape of the FIR SED are the dust temperature, the dust luminosity, the SFR, and fabs. Finally, we find a weak tendency for low-metallicity galaxies to have warmer and broader SEDs, while on the other hand high-metallicity galaxies have FIR SEDs that are colder and narrower.
Tautomerism: A Historical Perspective

When I started my PhDThesis, some 60 years ago, I was involved with pyrazoles tautomerism (annular of pyrazoles and indazoles and functional of pyrazolinones, then called pyrazolones). The knowledge about tautomerism was empirical, qualitative, and unsystematic. Owing to the efforts of Alan Roy Katritzky and those of my thesis supervisor, Robert Jacquier, the situation improved considerably resulting in the publication in 1976 of the book “TheTautomerismofHeterocycles” [1]. 1) One can say that, in 1976, there was good understanding of the thermodynamic aspects of the tautomerism of heteroaromatic compounds as well as that of compounds like β-diketones and related structures. Besides, thanks to the works of Hammett, Taft, Exner, and many others, the influence of substituents and solvents on the equilibrium constants can be quantified through extrathermodynamic relationships. We are now in the process of changing the paradigm and moving to a complete picture of the kinetic and thermodynamic aspects of tautomerism in the three phases, gas, solution, solid, and including ground and excited states.This is due to the increasing power of physicochemical methods as well as to the enormous success of theoretical chemistry. It is not finished yet andmany inputs andmany years of research will be necessary to declare that the task is completed.The subject has been reviewed in 2000 [2] and in 2014 [3] and the present book, to be published in 2016 [4], will further actualize it. An interesting effort is being made for buildingup tools for the automatic generation of all tautomeric forms of a given organic compound, for instance, Ambit-Tautomer [5]. To display a panorama of tautomerism today, a practical approach is to divide the knowledge into three sections: high, medium, and low. This warrants a separation of the thermodynamic aspects from the kinetic aspects, since often the first are well known but not so much the latter. Unless stated differently, this introduction will deal with prototropic tautomerism, which is by far the most common. It corresponds to the proton
Adaptable and conflict colouring multigraphs with no cycles of length three or four

The adaptable choosability of a multigraph G $G$ , denoted cha(G) ${\text{ch}}_{a}(G)$ , is the smallest integer k $k$ such that any edge labelling, τ $\tau $ , of G $G$ and any assignment of lists of size k $k$ to the vertices of G $G$ permits a list colouring, σ $\sigma $ , of G $G$ such that there is no edge e=uv $e=uv$ where τ(e)=σ(u)=σ(v) $\tau (e)=\sigma (u)=\sigma (v)$ . Here we show that for a multigraph G $G$ with maximum degree Δ ${\rm{\Delta }}$ and no cycles of length 3 or 4, cha(G)≤(22+o(1))Δ∕ln Δ ${\text{ch}}_{a}(G)\,\le (2\sqrt{2}+o(1))\sqrt{{\rm{\Delta }}\unicode{x02215}\mathrm{ln}\unicode{x0200A}{\rm{\Delta }}}$ . Under natural restrictions we can show that the same bound holds for the conflict choosability of G $G$ , which is a closely related parameter recently defined by Dvořák, Esperet, Kang and Ozeki.
Mitigating Imperfections in Mixed-Signal Neuromorphic Circuits

The progress in neuromorphic computing is fueled by the development of novel nonvolatile memories capable of storing analog information and implementing neural computation efficiently. However, like most other analog circuits, these devices and circuits are prone to imperfections, such as temperature dependency, noise, tuning error, etc., often leading to considerable performance degradation in neural network implementations. Indeed, imperfections are major obstacles in the path of further progress and ultimate commercialization of these technologies. Hence, a practically viable approach should be developed to deal with these nonidealities and unleash the full potential of nonvolatile memories in neuromorphic systems. Here, for the first time, we report a comprehensive characterization of critical imperfections in two analog-grade memories, namely passively-integrated memristors and redesigned eFlash memories, which both feature long-term retention, high endurance, analog storage, low-power operation, and compact nano-scale footprint. Then, we propose a holistic approach that includes modifications in the training, tuning algorithm, memory state optimization, and circuit design to mitigate these imperfections. Our proposed methodology is corroborated on a hybrid software/experimental framework using two benchmarks: a moderate-size convolutional neural network and ResNet-18 trained on CIFAR-10 and ImageNet datasets, respectively. Our proposed approaches allow 2.5x to 9x improvements in the energy consumption of memory arrays during inference and sub-percent accuracy drop across 25-100 C temperature range. The defect tolerance is improved by>100x, and a sub-percent accuracy drop is demonstrated in deep neural networks built with 64x64 passive memristive crossbars featuring 25% normalized switching threshold variations.
Analytical Inverter-Based Distributed Generator Model for Power Flow Analysis

Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous. Existing distribution system tools predominately model distributed generation sources as either negative PQ loads or as a PV generator and then employed a PV-PQ switching algorithm to mimic Volt/VAR support. These models neglect the unique characteristics of inverter-based distributed generation sources, have scalability and convergence issues, and are ill-suited for increasing solar penetration scenarios. This work proposes an inverter-based DG model accounting for the inverter's topology, sensing position, and control strategies. The model extends recently introduced analytical positive sequence generator models for three-phase studies. The use of circuit-simulation based heuristics help achieve robust convergence. Simulation of the PG&E prototypical feeders using a prototype solver demonstrate the model's accuracy and efficacy.
Improving Model Robustness with Latent Distribution Locally and Globally

—In this work, we consider model robustness of deep neural networks against adversarial attacks from a global manifold perspective. Leveraging both the local and global latent information, we propose a novel adversarial training method through robust optimization, and a tractable way to generate La- tent Manifold Adversarial Examples (LMAEs) via an adversarial game between a discriminator and a classiﬁer. The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner. ATLD preserves the local and global information of the latent manifold and promises improved robustness against adversarial attacks. To verify the effectiveness of our proposed method, we conduct extensive experiments over different datasets (e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g., PGD, CW), and show that our method substantially outperforms the state-of-the-art (e.g., Feature Scattering) in adversarial robustness by a large accuracy margin. The source codes are available at https://github.com/LitterQ/ATLD-pytorch.
Ultrafast Coherence Delocalization in Real Space Simulated by Polaritons

Coherence delocalization has been investigated on a coupled‐cavity molecular polariton platform in time, frequency, and spatial domains, enabled by ultrafast two‐dimensional infrared hyperspectral imaging. Unidirectional coherence delocalization (coherence prepared in one cavity transferred to another cavity) has been observed in frequency and real space. This directionality is enabled by the dissipation of delocalized photon from high‐energy to low‐energy modes, described by Lindblad dynamics. Further experiments show that when coherences are directly prepared between polaritons from different cavities, only energetically nearby polaritons can form coherences that survive the long‐range environmental fluctuation. Together with the Lindblad dynamics, this result implies that coherences delocalize through a one‐step mechanism where photons transfer from one cavity to another, shedding light to coherence evolution in natural and artificial quantum systems. This new optical platform based on molecular vibrational polariton thus demonstrates a way of combining photon and molecular modes to simulate coherence dynamics in the infrared regime.
Casimir densities induced by a sphere in the hyperbolic vacuum of de Sitter spacetime

Complete set of modes and the Hadamard function are constructed for a scalar field inside and outside a sphere in (D+1)-dimensional de Sitter spacetime foliated by negative constant curvature spaces. We assume that the field obeys Robin boundary condition on the sphere. The contributions in the Hadamard function induced by the sphere are explicitly separated and the vacuum expectation values (VEVs) of the field squared and energy-momentum tensor are investigated for the hyperbolic vacuum. In the flat spacetime limit the latter is reduced to the conformal vacuum in the Milne universe and is different from the maximally symmetric Bunch-Davies vacuum state. The vacuum energy-momentum tensor has a nonzero off-diagonal component that describes the energy flux in the radial direction. The latter is a purely sphere-induced effect and is absent in the boundary-free geometry. Depending on the constant in Robin boundary condition and also on the radial coordinate, the energy flux can be directed either from the sphere or towards the sphere. At early stages of the cosmological expansion the effects of the spacetime curvature on the sphere-induced VEVs are weak and the leading terms in the corresponding expansions coincide with those for a sphere in the Milne universe. The influence of the gravitational field is essential at late stages of the expansion. Depending on the field mass and the curvature coupling parameter, the decay of the sphere-induced VEVs, as functions of the time coordinate, is monotonic or damping oscillatory. At large distances from the sphere the fall-off of the sphere-induced VEVs, as functions of the geodesic distance, is exponential for both massless and massive fields.
Testing the ΛCDM paradigm with growth rate data and machine learning

The cosmological constant Λ and cold dark matter (CDM) model (ΛCDM) is one of the pillars of modern cosmology and is widely used as the de facto theoretical model by current and forthcoming surveys. As the nature of dark energy is very elusive, in order to avoid the problem of model bias, here we present a novel null test at the perturbation level that uses the growth of matter perturbation data in order to assess the concordance model. We analyze how accurate this null test can be reconstructed by using data from forthcoming surveys creating mock catalogs based on ΛCDM and three models that display a different evolution of the matter perturbations, namely a dark energy model with constant equation of state w (wCDM), the Hu & Sawicki and designer f(R) models, and we reconstruct them with a machine learning technique known as the Genetic Algorithms. We show that with future LSST-like mock data our consistency test will be able to rule out these viable cosmological models at more than 5σ, help to check for tensions in the data and alleviate the existing tension of the amplitude of matter fluctuations S 8 = σ 8 (Ωm,0/0.3)0.5.
Multilevel Stress Assessment From ECG in a Virtual Reality Environment Using Multimodal Fusion

Electrocardiogram (ECG) is an attractive option to assess stress in serious virtual reality (VR) applications due to its noninvasive nature. However, the existing machine learning (ML) models perform poorly. Moreover, existing studies only perform a binary stress assessment, while to develop a more engaging biofeedback-based application, multilevel assessment is necessary. Existing studies annotate and classify a single experience (e.g., watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. In this article, we report our findings on a new study on VR stress assessment, where three stress levels are assessed. ECG data were collected from nine users experiencing a VR roller coaster. The VR experience was then manually labeled in 10-s segments to three stress levels by three raters. We then propose a novel multimodal deep fusion model utilizing spectrogram and 1-D ECG that can provide a stress prediction from just a 1-s window. The experimental results demonstrate that the proposed model outperforms the classical heart-rate variability (HRV)-based ML models (9% increase in accuracy) and baseline deep learning models (2.5% increase in accuracy). We also report results on the benchmark WESAD dataset to show the supremacy of the model.
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems

In this paper, we study the problem of evaluating the addition of elements to a set. This problem is difficult, because it can, in the general case, not be reduced to unconditional preferences between the choices. Therefore, we model preferences based on the context of the decision. We discuss and compare two different Siamese network architectures for this task: a twin network that compares the two sets resulting after the addition, and a triplet network that models the contribution of each candidate to the existing set. We evaluate the two settings on a real-world task; learning human card preferences for deck building in the collectible card game Magic: The Gathering. We show that the triplet approach achieves a better result than the twin network and that both outperform previous results on this task.
A Grounded Theory of Cognitive Load Drivers in Novice Agile Software Development Teams

Objective: The purpose of this paper is to identify the largest cognitive challenges faced by novices developing software in teams. Method: Using grounded theory, we conducted an ethnographic study for two months following four ten person novice teams, consisting of computer science students, developing software systems. Result: This paper identifies version control and merge operations as the largest challenge faced by the novices. The literature studies reveal that little research appears to have been carried out in the area of version control from a user perspective. Limitations: A qualitative study on students is not applicable in all contexts, but the result is credible and grounded in data and substantiated by extant literature. Conclusion: We conclude that our findings motivate further research on cognitive perspectives to guide improvement of software engineering and its tools.
A Hubble Space Telescope Imaging Survey of Low-redshift Swift-BAT Active Galaxies

We present initial results from a Hubble Space Telescope snapshot imaging survey of the host galaxies of Swift-BAT active galactic nuclei (AGN) at z < 0.1. The hard X-ray selection makes this sample relatively unbiased in terms of obscuration, compared to optical AGN selection methods. The high-resolution images of 154 target AGN enable us to investigate the detailed photometric structure of the host galaxies, such as the Hubble type and merging features. We find 48% and 44% of the sample to be hosted by early-type and late-type galaxies, respectively. The host galaxies of the remaining 8% of the sample are classified as peculiar galaxies because they are heavily disturbed. Only a minor fraction of host galaxies (18%–25%) exhibit merging features (e.g., tidal tails, shells, or major disturbance). The merging fraction increases strongly as a function of bolometric AGN luminosity, revealing that merging plays an important role in triggering luminous AGN in this sample. However, the merging fraction is weakly correlated with the Eddington ratio, suggesting that merging does not necessarily lead to an enhanced Eddington ratio. Type 1 and Type 2 AGN are almost indistinguishable in terms of their Hubble type distribution and merging fraction. However, the merging fraction of Type 2 AGN peaks at a lower bolometric luminosity compared with those of Type 1 AGN. This result may imply that the triggering mechanism and evolutionary stages of Type 1 and Type 2 AGN are not identical.
Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

Author name ambiguity remains a critical open problem in digital libraries due to synonymy and homonymy of names. In this paper, we propose a novel approach to link author names to their real-world entities by relying on their co-authorship pattern and area of research. Our supervised deep learning model identifies an author by capturing his/her relationship with his/her co-authors and area of research, which is represented by the titles and sources of the target author's publications. These attributes are encoded by their semantic and symbolic representations. To this end, Bib2Auth uses ~ 22K bibliographic records from the DBLP repository and is trained with each pair of co-authors. The extensive experiments have proved the capability of the approach to distinguish between authors sharing the same name and recognize authors with different name variations. Bib2Auth has shown good performance on a relatively large dataset, which qualifies it to be directly integrated into bibliographic indices.
Revealing the competition between charge density wave and superconductivity in 
CsV3Sb5
 through uniaxial strain

In this paper we report the effect of uniaxial strain ε applied along the crystalline a axis on the newly discovered kagome superconductor CsV 3 Sb 5 . At ambient conditions, CsV 3 Sb 5 shows a charge-density wave (CDW) transition at T CDW = 94 . 5 K and superconducts below T c = 3 . 34 K. In our paper, when the uniaxial strain ε is varied from − 0 . 90% to 0 . 90% , T c monotonically increases by ∼ 33% from 3.0 to 4.0 K, giving rise to the empirical relation T c ( ε ) = 3 . 4 + 0 . 56 ε + 0 . 12 ε 2 . On the other hand, for ε changing from − 0 . 76% to 1 . 26% , T CDW decreases monotonically by ∼ 10% from 97.5 to 87.5 K with T CDW ( ε ) = 94 . 5 − 4 . 72 ε − 0 . 60 ε 2 . The opposite response of T c and T CDW to the uniaxial strain suggests strong competition between these two orders. Comparison with hydrostatic pressure measurements indicate that it is the change in the c axis that is responsible for these behaviors of the CDW and superconducting transitions, and that the explicit breaking of the sixfold rotational symmetry by strain has a negligible effect. Combined with our ﬁrst-principles calculations and phenomenological analysis, we conclude that the enhancement in T c with decreasing c is caused primarily by the suppression of T CDW , rather than strain-induced modiﬁcations in the bare superconducting parameters. We propose that the sensitivity of T CDW with respect to the changes in the c axis arises from the impact of the latter on the trilinear coupling between the M + 1 and the L − 2 phonon modes associated with the CDW. Overall, our paper reveals that the c -axis lattice parameter, which can be controlled by both pressure and uniaxial strain, is a powerful tuning knob for the phase diagram of CsV 3 Sb 5 .
Training a Deep Neural Network via Policy Gradients for Blind Source Separation in Polyphonic Music Recordings

We propose a method for the blind separation of sounds of musical instruments in audio signals. We describe the individual tones via a parametric model, training a dictionary to capture the relative amplitudes of the harmonics. The model parameters are predicted via a U-Net, which is a type of deep neural network. The network is trained without ground truth information, based on the difference between the model prediction and the individual STFT time frames. Since some of the model parameters do not yield a useful backpropagation gradient, we model them stochastically and employ the policy gradient instead. To provide phase information and account for inaccuracies in the dictionary-based representation, we also let the network output a direct prediction, which we then use to resynthesize the audio signals for the individual instruments. Due to the flexibility of the neural network, inharmonicity can be incorporated seamlessly and no preprocessing of the input spectra is required. Our algorithm yields high-quality separation results with particularly low interference on a variety of different audio samples, both acoustic and synthetic, provided that the sample contains enough data for the training and that the spectral characteristics of the musical instruments are sufficiently stable to be approximated by the dictionary.
Breaking of ensemble equivalence for dense random graphs under a single constraint

Abstract Two ensembles are frequently used to model random graphs subject to constraints: the microcanonical ensemble (= hard constraint) and the canonical ensemble (= soft constraint). It is said that breaking of ensemble equivalence (BEE) occurs when the specific relative entropy of the two ensembles does not vanish as the size of the graph tends to infinity. Various examples have been analysed in the literature. It was found that BEE is the rule rather than the exception for two classes of constraints: sparse random graphs when the number of constraints is of the order of the number of vertices, and dense random graphs when there are two or more constraints that are frustrated. We establish BEE for a third class: dense random graphs with a single constraint on the density of a given simple graph. We show that BEE occurs in a certain range of choices for the density and the number of edges of the simple graph, which we refer to as the BEE-phase. We also show that, in part of the BEE-phase, there is a gap between the scaling limits of the averages of the maximal eigenvalue of the adjacency matrix of the random graph under the two ensembles, a property that is referred to as the spectral signature of BEE. We further show that in the replica symmetric region of the BEE-phase, BEE is due to the coexistence of two densities in the canonical ensemble.