Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer Gaia Data Release 2. Observational Hertzsprung-Russell diagrams

Context. Gaia Data Release 2 provides high-precision astrometry and three-band photometry for about 1.3 billion sources over the full sky. The precision, accuracy, and homogeneity of both astrometry and photometry are unprecedented. Aims. We highlight the power of the Gaia DR2 in studying many ﬁne structures of the Hertzsprung-Russell diagram (HRD). Gaia allows us to present many di ﬀ erent HRDs, depending in particular on stellar population selections. We do not aim here for completeness in terms of types of stars or stellar evolutionary aspects. Instead, we have chosen several illustrative examples. Methods. We describe some of the selections that can be made in Gaia DR2 to highlight the main structures of the Gaia HRDs. We select both ﬁeld and cluster (open and globular) stars, compare the observations with previous classiﬁcations and with stellar evolutionary tracks, and we present variations of the Gaia HRD with age, metallicity, and kinematics. Late stages of stellar evolution such as hot subdwarfs, post-AGB stars, planetary nebulae, and white dwarfs are also analysed, as well as low-mass brown dwarf objects. Results. The Gaia HRDs are unprecedented in both precision and coverage of the various Milky Way stellar populations and stellar evolutionary phases. Many ﬁne structures of the HRDs are presented. The clear split of the white dwarf sequence into hydrogen and helium white dwarfs is presented for the ﬁrst time in an HRD. The relation between kinematics and the HRD is nicely illustrated. Two di ﬀ erent populations in a classical kinematic selection of the halo are unambiguously identiﬁed in the HRD. Membership and mean parameters for a selected list of open clusters are provided. They allow drawing very detailed cluster sequences, highlighting ﬁne structures, and providing extremely precise empirical isochrones that will lead to more insight in stellar physics. Conclusions. Gaia DR2 demonstrates the potential of combining precise astrometry and photometry for large samples for studies in stellar evolution and stellar population and opens an entire new area for HRD-based studies.
PHYSICAL, CHEMICAL AND SENSORY PROPERTIES OF FRESH ORANGE JUICE FORTIFIED WITH REISHI (Ganoderma lucidum) EXTRACT

In this study, fresh orange juice fortified with reishi (Ganoderma lucidum) extract was produced and its quality parameters were investigated. 330 mL and 200 mL of fresh orange juice samples containing different amounts (5 or 10 mL) of reishi extract were formulated. Brix and pH of the samples ranged from 10.9±0.8 to 11.5±1.2, and 3.66 to 3.68, respectively. Ascorbic acid content (mg/100mL) of the samples was between 54.5±2.2 and 64.8±8.5, and total carbohydrate contents (g/L) were found as 12.96±4.19 17.34±4.94. Total phenolic contents of the samples were determined as 313.6±3.3 329.5±3.3 μg GAE/100 mL. Color measurements indicated no significant difference between the control and fortified samples. Five-point hedonic scale (65 volunteers) and ranking tests (29 volunteers) showed that an alternative functional beverage with acceptable quality properties was produced when recommended daily intake dose of reishi extract (10 mL/day) was added to 330 mL of orange juice.
Novel View Synthesis Based on View-dependent Texture Mapping with Geometry-aware Color Continuity( VR Psychology VI)

Novel view synthesis (NVS) is renowned for its variety of applications, and recent techniques are based on an image-based approach, which uses multiple input images to synthesize novel views, with the help of 3D geometry. View-dependent texture mapping is one of the most well-known techniques of this kind. However, it sometimes suffers from blurry or duplicated edges when the geometry is inaccurate because it blends several input images for alleviating inconsistent lighting conditions among input images. This paper presents a view-dependent texture mapping-based method for NVS that leverages geometry-aware color continuity in texture selection. Our color continuity constraint suppresses noticeable texture boundaries in resulting novel views without blending, while our method relaxes the constraint on steep geometric changes. Since such geometric changes are often followed by surface property changes, our new color continuity constraint can lead to more natural novel views. We formulate our texture selection by an energy minimization problem and solve it with the graph cut algorithm. Our experimental results have demonstrated the superiority of our method.
AC losses in HTS coils for high-frequency and non-sinusoidal currents

AC losses in racetrack coils that are wound of YBCO tapes are measured for sinusoidal and non-sinusoidal transport currents with fundamental frequencies up to 1 kHz. An electrical method to measure losses for non-sinusoidal currents is developed for this purpose. The measured losses are compared to the losses calculated by 2D finite element models with power-law material models. The frequency and waveform-dependency of the measured losses are shown and compared to the results of the models over a wide range of frequencies and waveforms. Finally, it is shown that the finite element models can accurately predict AC losses resulting from non-sinusoidal transport currents as are present in highly dynamic motors with AC armature coils.
Mode locking of a fibre laser with a matrix-less carbon nanotube film

This work for the first time reports experimental results on Er fibre laser mode-locked with saturable absorbers based on single-walled matrix-less carbon nanotube films fabricated by aerosol method. Nearly transform-limited output pulses had duration of 1.7 ps and average power of 0.5 mW. Measured high-contrast RF output spectra (noise suppression over 70 dB in 1-GHz band) confirm high quality of achieved mode locking. Discussed are details of the aerosol method of matrix-less carbon nanotube film fabrication and parameters of studied mode-locked fibre laser. Application of matrixless carbon nanotube films for mode locking and Q-switching of fibre and other laser types are analysed.
L G ] 2 1 M ay 2 01 8 A New Lower Bound for Agnostic Learning with Sample Compression Schemes

Compression-based arguments provide some of the simplest and tightest generalization bounds in the literature. These are known as Occam learning in the most general setting [Blumer et al., 1989], and the special case of sample compression Littlestone and Warmuth [1986], Devroye et al. [1996], Graepel et al. [2005], Floyd and Warmuth [1995] has been receiving a fair amount of recent attention [Moran and Yehudayoff, 2016, David et al., 2016, Zhivotovskiy, 2017, Hanneke et al., 2018].
Convergence analysis of surrogate-based methods for Bayesian inverse problems

The major challenges in the Bayesian inverse problems arise from the need for repeated evaluations of the forward model, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. Many attempts at accelerating Bayesian inference have relied on surrogates for the forward model, typically constructed through repeated forward simulations that are performed in an offline phase. Although such approaches can be quite effective at reducing computation cost, there has been little analysis of the approximation on posterior inference. In this work, we prove error bounds on the Kullback–Leibler (KL) distance between the true posterior distribution and the approximation based on surrogate models. Our rigorous error analysis show that if the forward model approximation converges at certain rate in the prior-weighted L2 norm, then the posterior distribution generated by the approximation converges to the true posterior at least two times faster in the KL sense. The error bound on the Hellinger distance is also provided. To provide concrete examples focusing on the use of the surrogate model based methods, we present an efficient technique for constructing stochastic surrogate models to accelerate the Bayesian inference approach. The Christoffel least squares algorithms, based on generalized polynomial chaos, are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. The numerical strategy and the predicted convergence rates are then demonstrated on the nonlinear inverse problems, involving the inference of parameters appearing in partial differential equations.
A Fitness Function Elimination Theory For Blackbox Optimization And Problem Class Learning

The modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox optimization from which, we conclude that of these three desired characteristics, only two can be maximized by any algorithm. We put forward an alternate view of optimization where we use knowledge about the problem class and samples from the problem instance to identify which problem instances from the class are being solved. From this Elimination of Fitness Functions approach, an idealized optimization algorithm that minimizes sample counts over any problem class, given complete knowledge about the class, is designed. This theory allows us to learn more about the difficulty of various problems, and we are able to use it to develop problem complexity bounds. We present general methods to model this algorithm over a particular problem class and gain efficiency at the cost of specifically targeting that class. This is demonstrated over the Generalized Leading-Ones problem and a generalization called LO∗∗, and efficient algorithms with optimal performance are derived and analyzed. We also
The Credibility Crisis in IS: A Global Stakeholder Perspective

The purpose of this panel involves helping the IS community devise strategies for augmenting the field’s credibility. Representing different continents, educational systems, and roles, our panelists will provide a global perspective on IS credibility. Using stakeholder theory as an organizing framework, this panel will identify the key stakeholders that positively and negatively influence the IS discipline as well as strategies for leveraging these stakeholders. Spirited debates will occur concerning the role of regulators, funding sources, faculty, administrators, students, and employers in shaping the credibility of the IS discipline.
Spatially-resolved SFR in nearby disk galaxies using IFS data

Abstract Exploring the spatial distribution of the star formation rate (SFR) in nearby galaxies is essential to understand their evolution through cosmic time. With this aim in mind, we use a representative sample that contains a variety of morphological types, the CALIFA Integral Field Spectroscopy (IFS) sample. Previous to this work, we have verified that our extinction-corrected Hα measurements successfully reproduce the values derived from other SFR tracers such as Hα obs + IR or UV obs + IR (Catalán-Torrecilla et al. 2015). Now, we go one step further applying 2-dimensional photometric decompositions (Méndez-Abreu et al. (2008), Méndez-Abreu et al. (2014)) over these datacubes. This method allows us to obtain the amount of SFR in the central part (bulge or nuclear source), the bar and the disk, separately. First, we determine the light coming from each component as the ratio between the luminosity in every component (bulge, bar or disk) and the total luminosity of the galaxy. Then, for each galaxy we multiply the IFS datacubes by these previous factors to recover the luminosity in each component. Finally, we derive the spectrum associated to each galaxy component integrating the spatial information in the weighted datacube using an elliptical aperture covering the whole galaxy. 2D photometric decomposition applied over 3D datacubes will give us a more detailed understanding of the role that disks play in more massive galaxies. Knowing if the disks in more massive SF galaxies have on average a lower or higher level of star formation activity and how these results are affected by the presence of nuclear bars are still open questions that we can now solve. We describe the behavior of these components in the SFR vs. stellar mass diagram. In particular, we highlight the role of the disks and their contribution to both the integrated SFR for the whole galaxy and the SFR in the disk at different stellar masses in the SFR vs. stellar mass diagram together with their relative position to the star forming Main Sequence.
A Variational Inequality Perspective on Generative Adversarial Nets

Stability has been a recurrent issue in training generative adversarial networks (GANs). One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods specifically designed for this adversarial training. In this work, we review the “variational inequality” framework which contains most formulations of the GAN objective introduced so far. Taping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend standard methods designed for variational inequalities to GANs training, such as a stochastic version of the extragradient method, and empirically investigate their behavior on GANs.
Quadratic Zeeman effect and spin-lattice relaxation of Tm3+:YAG at high magnetic fields

Anisotropy of the quadratic Zeeman effect for the $^3{\rm H}_6 \rightarrow \, ^3{\rm H}_4$ transition at 793 nm wavelength in $^{169}$Tm$^{3+}$-doped Y$_3$Al$_5$O$_{12}$ is studied, revealing shifts ranging from near zero up to + 4.69 GHz/T$^2$ for ions in magnetically inequivalent sites. This large range of shifts is used to spectrally resolve different subsets of ions and study nuclear spin relaxation as a function of temperature, magnetic field strength, and orientation in a site-selective manner. A rapid decrease in spin lifetime is found at large magnetic fields, revealing the weak contribution of direct-phonon absorption and emission to the nuclear spin-lattice relaxation rate. We furthermore confirm theoretical predictions for the phonon coupling strength, finding much smaller values than those estimated in the limited number of past studies of thulium in similar crystals. Finally, we observe a significant -- and unexpected -- magnetic field dependence of the two-phonon Orbach spin relaxation process at higher field strengths, which we explain through changes in the electronic energy-level splitting arising from the quadratic Zeeman effect.
Quantitative Method for Estimating Driver Eye Height

The issue of what drivers can or cannot see while operating their vehicle is critical for avoiding roadway hazards. One factor is eye-height, which although varies between individuals, is often not available to measure directly. We present a method of calculating driver eye-height using an anthropometry dataset as well as vehicle specifications obtained from online resources. In this study, participants sat in various passenger vehicles and had their seated eye-height measured, which was then compared to their calculated eye-height. Results suggest that our methodology is accurate for larger passenger vehicles, though it slightly underestimates driver eye-height for smaller passenger vehicles. Implications for these results, as well as directions for future studies are discussed in the paper.
Calderón's inverse problem with a finite number of measurements II: independent data

ABSTRACT We prove a local Lipschitz stability estimate for Gel'fand-Calderón's inverse problem for the Schrödinger equation. The main novelty is that only a finite number of boundary input data is available, and those are independent of the unknown potential, provided it belongs to a known finite-dimensional subspace of . A similar result for Calderón's problem is obtained as a corollary. This improves upon two previous results of the authors on several aspects, namely the number of measurements and the stability with respect to mismodeling errors. A new iterative reconstruction scheme based on the stability result is also presented, for which we prove exponential convergence in the number of iterations and stability with respect to noise in the data and to mismodeling errors.
Guiding Reinforcement Learning Exploration Using Natural Language Extended Abstract

In this work we present a technique for using natural language to help reinforcement learning generalize to unseen environments using neural machine translation techniques. These techniques are then integrated into policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, and show that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.
BASED OPTIMALITY CONDITIONS AND APPROXIMATE SOLUTION OF A DETERMINISTIC INFINITE HORIZON DISCOUNTED OPTIMAL CONTROL PROBLEM IN DISCRETE TIME

It has been recently established that a deterministic infinite horizon discounted optimal control problem in discrete time is closely related to a certain infinite dimensional linear programming problem and its dual, the latter taking the form of a certain max-min problem. In the present paper, we use these results to establish necessary and sufficient optimality conditions for this optimal control problem and to investigate a way how the latter can be used for the construction of a near optimal control.
Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human-Robot-Interface

Exoskeletons and orthoses are wearable mobile systems pro- viding mechanical beneﬁts to the users. Despite signiﬁcant im-provements in the last decades, the technology is not fully mature to be adopted for strenuous and non-programmed tasks. To accommodate this insufﬁciency, different aspects of this technology need to be analysed and improved. Numerous studies have been trying to address some aspects of exoskeletons, e.g. mechanism design, intent prediction, and control scheme. However, most works have focused on a speciﬁc element of design or application without providing a comprehensive review framework. This study aims to analyse and survey the contributing aspects to the improvement and broad adoption of this technology. To address this, after introducing assistive devices and exoskeletons, the main design criteria will be investigated from a physical Human-Robot Interface (HRI) perspective. The study will be further developed by outlining several examples of known assistive devices in different categories. In order to establish an intelligent HRI strategy and enabling intuitive control for users, cognitive HRI will be investigated. Various approaches to this strategy will be reviewed, and a model for intent prediction will be proposed. This model is utilised to predict the gate phase from a single Electromyography (EMG) channel input. The outcomes of modelling show the potential use of single-channel input in low- power assistive devices. Furthermore, the proposed model can provide redundancy in devices with a complex control strategy.
Activation Maximization Generative Adversarial Nets

In this paper, we present a systematic study on GANs with categorical discriminator, especially their impact on the optimization scheme of the generator. We derive class-aware gradients and cross-entropy decomposition, to theoretically reveal how they help GAN training and the inherent problems in previous models. Based on the analysis, we propose an advanced model AM-GAN, along with an interesting dynamic labeling mechanism. We show mathematically that the proposed AM-GAN is a general one covering several major existing solutions that exploit categorical discriminator. Empirical experiments demonstrate the effectiveness of the proposed method, with state-of-the-art sample quality and fast convergence.
1 Image Predict ( Molecular Descriptors ) Compute Chemception ChemNet Small Dataset ( ~ 1 k-10 k entries ) Image Predict ( Molecular Properties ) Transfer Learning

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet’s accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally eective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is eective across multiple data modalities. Our results indicate a pretrained ChemNet that incorporates chemistry domain knowledge, enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.
Tagging like Humans : Diverse and Distinct Image Annotation ( Supplementary Material )

Here we present some qualitative results of to compare the performance of DIA [3] and our proposed method DIA-GAN. The results on ESP Game [2] data are shown in Fig. 3, and those on IAPRTC-12 [1] data are shown in Fig. 4 (see the last page). In each sub-figure, the left is the original image, while the right shows the ground-truth complete tag list and the annotation results of DIA and DIA-GAN. For each method, we present two cases, including 3 tags (i.e., each single tag subset includes at most 3 tags) and 5 tags (i.e., each single tag subset includes at most 5 tags). At each case, we present at most 3 tag subsets. As described in Section 5.5 of the main manuscript, the DPP sampling process in each method (DIA or DIA-GAN) is run 10 times to obtain 10 tag subsets. Then 3 subsets with the largest tag weight summations are picked as the presented results. If there are same subsets in these 3 subsets, the redundant subsets are removed. Furthermore, we also present the F1−sp score of the ensemble subset of these picked subsets. From these qualitative results, we can see that (1) in each single tag subset of both DIA and DIA-GAN, the included tags are semantically distinct to each other; (2) the tag subsets of DIA-GAN are more diverse than those of DIA, so the corresponding ensemble tag subset of DIA-GAN covers more semantic meanings than that of DIA.