Miracle-dz/UniFAR-FTUs huggingface dataset:
High-precision abundances of elements in solar-type stars

Aims. Previous high-precision studies of abundances of elements in solar twin stars are extended to a wider metallicity range to see how the trends of element ratios with stellar age depend on [Fe/H]. Methods. HARPS spectra with signal-to-noise ratios S/N ≳ 600 at λ​ ∼ ​6000 Å were analysed with MARCS model atmospheres to obtain 1D LTE abundances of C, O, Na, Mg, Al, Si, Ca, Ti, Cr, Fe, Ni, Sr, and Y for 72 nearby solar-type stars with metallicities in the range of −0.3 ≲ [Fe/H] ≲ +0.3 and ASTEC stellar models were used to determine stellar ages from effective temperatures, luminosities obtained via Gaia DR2 parallaxes, and heavy element abundances. Results. The age-metallicity distribution appears to consist of the following two distinct populations: a sequence of old stars with a steep rise of [Fe/H] to ∼​ + 0.3 dex at an age of ∼​7 Gyr and a younger sequence with [Fe/H] increasing from about −0.3 dex to ∼​ + 0.2 dex over the last 6 Gyr. Furthermore, the trends of several abundance ratios, [O/Fe], [Na/Fe], [Ca/Fe], and [Ni/Fe], as a function of stellar age, split into two corresponding sequences. The [Y/Mg]-age relation, on the other hand, shows no offset between the two age sequences and has no significant dependence on [Fe/H], but the components of a visual binary star, ζ Reticuli, have a large and puzzling deviation. Conclusions. The split of the age-metallicity distribution into two sequences may be interpreted as evidence of two episodes of accretion of gas onto the Galactic disk with a quenching of star formation in between. Some of the [X/Fe]-age relations support this scenario but other relations are not so easy to explain, which calls for a deeper study of systematic errors in the derived abundances as a function of [Fe/H], in particular 3D non-LTE effects.
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible for learning in real neural circuits. An alternative called target propagation proposes to solve this implausibility by using a top-down model of neural activity to convert an error at the output of a neural network into layer-wise and plausible 'targets' for every unit. These targets can then be used to produce weight updates for network training. However, thus far, target propagation has been heuristically proposed without demonstrable equivalence to backpropagation. Here, we derive an exact correspondence between backpropagation and a modified form of target propagation (GAIT-prop) where the target is a small perturbation of the forward pass. Specifically, backpropagation and GAIT-prop give identical updates when synaptic weight matrices are orthogonal. In a series of simple computer vision experiments, we show near-identical performance between backpropagation and GAIT-prop with a soft orthogonality-inducing regularizer.
A Novel Meta-Heuristic Optimization Algorithm Inspired by the Spread of Viruses

According to the no-free-lunch theorem, there is no single meta-heuristic algorithm that can optimally solve all optimization problems. This motivates many researchers to continuously develop new optimization algorithms. In this paper, a novel nature-inspired meta-heuristic optimization algorithm called virus spread optimization (VSO) is proposed. VSO loosely mimics the spread of viruses among hosts, and can be effectively applied to solving many challenging and continuous optimization problems. We devise a new representation scheme and viral operations that are radically different from previously proposed virus-based optimization algorithms. First, the viral RNA of each host in VSO denotes a potential solution for which different viral operations will help to diversify the searching strategies in order to largely enhance the solution quality. In addition, an imported infection mechanism, inheriting the searched optima from another colony, is introduced to possibly avoid the prematuration of any potential solution in solving complex problems. VSO has an excellent capability to conduct adaptive neighborhood searches around the discovered optima for achieving better solutions. Furthermore, with a flexible infection mechanism, VSO can quickly escape from local optima. To clearly demonstrate both its effectiveness and efficiency, VSO is critically evaluated on a series of well-known benchmark functions. Moreover, VSO is validated on its applicability through two real-world examples including the financial portfolio optimization and optimization of hyper-parameters of support vector machines for classification problems. The results show that VSO has attained superior performance in terms of solution fitness, convergence rate, scalability, reliability, and flexibility when compared to those results of the conventional as well as state-of-the-art meta-heuristic optimization algorithms.
Blissful Ignorance: Anti-Transfer Learning for Task Invariance

We introduce the novel concept of anti-transfer learning for neural networks. While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning representations that have been learned for a different task, which is not relevant and potentially misleading for the new task and should be ignored. Examples of such tasks are style vs content recognition or pitch vs timbre from audio. By penalizing similarity between the second network and the previously learned features, co-incidental correlations between the target and the unrelated task can be avoided, yielding more reliable representations and better performance on the target task. We implemented anti-transfer learning with different similarity metrics and aggregation functions. We evaluate the approach in the audio domain with different tasks and setups, using four datasets in total. The results show that anti-transfer learning consistently improves accuracy in all test cases, proving that it can push the network to learn more representative features for the task at hand.
Strong coupling between a single quantum dot and an L4/3 photonic crystal nanocavity

We demonstrate strong coupling between a single quantum dot (QD) and a GaAs-based L4/3-type photonic crystal nanocavity. The L4/3 cavity supports a high theoretical Q factor (∼8 × 106), a small mode volume [∼0.32 (λ/n)3], and an electric field distribution with the maximum electric field lying within the host dielectric material, which facilitates strong coupling with a QD. We fabricated L4/3 cavities and observed a high Q factor over 80 000 using photoluminescence measurement. We confirmed strong coupling between a single QD and an L4/3 cavity with a Q factor of 33 000 by observing a clear anti-crossing in the spectra.
Adaptive Reward-Free Exploration

Reward-free exploration is a reinforcement learning setting recently studied by Jin et al., who address it by running several algorithms with regret guarantees in parallel. In our work, we instead propose a more adaptive approach for reward-free exploration which directly reduces upper bounds on the maximum MDP estimation error. We show that, interestingly, our reward-free UCRL algorithm can be seen as a variant of an algorithm of Fiechter from 1994 [11], originally proposed for a different objective that we call best-policy identification. We prove that RF-UCRL needs O (SAH 4 /e 2) log(1/δ) episodes to output, with probability 1 − δ, an e-approximation of the optimal policy for any reward function. We empirically compare it to oracle strategies using a generative model.
Hemolytic Properties of Hemocyanin from Mud Crab Scylla serrata

ABSTRACT Recently, infectious diseases have seriously inhibited the aquaculture of mud crab Scylla serrata in southeastern China. A better understanding of the immune molecules and defense mechanisms may be beneficial in reducing the harmful nature of these diseases. Available data show that hemocyanin (HMC) is a copper-containing respiratory protein present in the hemolymph of both mollusc and arthropod, and that it plays multiple roles in immune defense. In the current study, HMC from S. serrata (HMC-C) was isolated, and its hemolytic properties were investigated. The HMC-C shows hemolytic activities against vertebrate erythrocytes. The hemolysis displays dependency on pH, temperature, divalent cation, and HMC-C concentration. Complete hemolysis occurred at a concentration of 0.1 mg/mL, pH 5.0, and temperature of 37°C in the presence of calcium. Furthermore, all 5 subunits of HMC-C were detected in the solubilized incubation products of erythrocytes with HMC-C, and the hemolysis could be inhibited to different degrees by osmoprotectants of various molecular masses. Together, our data suggest that HMC-C mediates hemolysis by inserting all 5 subunits into the erythrocyte membrane, causing cell rupture through a colloidosmotic mechanism.
Achieving robustness in classification using optimal transport with hinge regularization

Adversarial examples have pointed out Deep Neural Network’s vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed approach provides the expected guarantees in terms of robustness without any significant accuracy drop. The adversarial examples, on the proposed models, visibly and meaningfully change the input providing an explanation for the classification.
Fingerprinting Analog IoT Sensors for Secret-Free Authentication

Especially in context of critical urban infrastructures, trust in IoT data is of utmost importance. While most technology stacks provide means for authentication and encryption of device-to-cloud traffic, there are currently no mechanisms to rule out physical tampering with an IoT device’s sensors. Addressing this gap, we introduce a new method for extracting a hardware fingerprint of an IoT sensor which can be used for secret-free authentication. By comparing the fingerprint against reference measurements recorded prior to deployment, we can tell whether the sensing hardware connected to the IoT device has been changed by environmental effects or with malicious intent. Our approach exploits the characteristic behavior of analog circuits, which is revealed by applying a fixed-frequency alternating current to the sensor, while recording its output voltage. To demonstrate the general feasibility of our method, we apply it to four commercially available temperature sensors using laboratory equipment and evaluate the accuracy. The results indicate that with a sensible configuration of the two hyperparameters we can identify individual sensors with high probability, using only a few recordings from the target device.
A single far-field uniquely determines the shape of a scattering screen

We consider the problem of fixed frequency acoustic scattering from a sound-soft flat screen. More precisely the obstacle is restricted to a two-dimensional plane and it scatters acoustic waves to three-dimensional space. The model is particularly relevant in the study and design of reflecting sonars and antennas. Our main result is that given the plane where the screen is located, a single far-field pattern determines the exact shape of the screen. It is true even for screens whose shape is an arbitrary simply connected smooth domain. This is in stark contrast with earlier single measurement inverse scattering results where only polygonal scatterers are determined, or other very restrictive a priori conditions are imposed.
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network

Multi-hop reading comprehension across multiple documents attracts much attentions recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by the human reasoning processing, we introduce a path-based graph with reasoning paths which extracted from supporting documents. The path-based graph can combine both the idea of the graph-based and path-based approaches, so it is better for multi-hop reasoning. Meanwhile, we propose Gated-GCN to accumulate evidences on the path-based graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning. We evaluate our approach on WikiHop dataset, and our approach achieves the the-state-of-art accuracy against previous published approaches. Especially, our ensemble model surpasses the human performance by 4.2%.
Long short-term memory networks for proton dose calculation in highly heterogeneous tissues.

PURPOSE To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANN) in challenging 3D anatomies. METHODS A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of (2D) slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies. RESULTS For artificial phantom cases, the trained LSTM model achieved a 98:57% γ -index pass rate ([1%, 3mm]) in comparison to MC simulations for a pencil beam with initial energy 104:25MeV. For a lung patient case, we observe pass rates of 98:56%, 97:74%, and 94:51% for an initial energy of 67:85MeV, 104:25MeV, and 134:68MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average - γ index pass rate of 97:85%. CONCLUSIONS LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted.
Photonic Bound States in the Continuum in Si Structures with the Self‐Assembled Ge Nanoislands

Germanium self‐assembled nanoislands and quantum dots are very prospective for CMOS‐compatible optoelectronic integrated circuits but their photoluminescence (PL) intensity is still insufficient for many practical applications. Here, it is demonstrated experimentally that the PL of Ge nanoislands in silicon photonic crystal slabs (PCS) with hexagonal lattice can be dramatically enhanced due to the involvement in the emission process of the bounds states in the continuum. These high‐Q photonic resonances allow to achieve PL resonant peaks with the quality factor as high as 2200 and with the peak PL enhancement factor of more than two orders of magnitude. The corresponding integrated PL enhancement is demonstrated to be more than one order of magnitude. This effect is studied theoretically by the Fourier modal method in the scattering matrix form. The symmetry of the quasi‐normal guided modes in the PCS is described in terms of group theory. This work paves the way toward a new class of optoelectronic components compatible with silicon technology.
Sample-Efficient Low Rank Phase Retrieval

This work studies the Low Rank Phase Retrieval (LRPR) problem: recover an <inline-formula> <tex-math notation="LaTeX">$n \times q$ </tex-math></inline-formula> rank-<inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> matrix <inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {X}^{\ast}}$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}_{k} = | \boldsymbol {A}_{k}^\top \boldsymbol {x}^{\ast} _{k}|$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$k=1, 2,\ldots, q$ </tex-math></inline-formula>, when each <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}_{k}$ </tex-math></inline-formula> is an m-length vector containing independent phaseless linear projections of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}^{\ast}_{k}$ </tex-math></inline-formula>. Here <inline-formula> <tex-math notation="LaTeX">$|. |$ </tex-math></inline-formula> takes element-wise magnitudes of a vector. The different matrices <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {A}_{k}$ </tex-math></inline-formula> are i.i. d. and each contains i.i. d. standard Gaussian entries. We obtain an improved guarantee for AltMinLowRaP, which is an Alternating Minimization solution to LRPR that was introduced and studied in our recent work. As long as the right singular vectors of <inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {X}^{\ast}}$ </tex-math></inline-formula> satisfy the incoherence assumption, we can show that the AltMinLowRaP estimate converges geometrically to <inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {X}^{\ast}}$ </tex-math></inline-formula> if the total number of measurements <inline-formula> <tex-math notation="LaTeX">$mq \gtrsim nr^{2} (r + \log (1/\epsilon))$ </tex-math></inline-formula>. In addition, we also need <inline-formula> <tex-math notation="LaTeX">$m \gtrsim max(r, \log q, \log n)$ </tex-math></inline-formula> because of the specific asymmetric nature of our problem. Compared to our recent work, we improve the sample complexity of the AltMin iterations by a factor of <inline-formula> <tex-math notation="LaTeX">$r^{2}$ </tex-math></inline-formula>, and that of the initialization by a factor of <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>. We argue, based on comparison with related well-studied problems, why the above sample complexity cannot be improved any further for non-convex solutions to LRPR. We also extend our result to the noisy case; we prove stability to corruption by small additive noise.
Bright, polarization-tunable high repetition rate extreme ultraviolet beamline for coincidence electron–ion imaging

After decades of supremacy of the Titanium:Sapphire technology, Ytterbium-based high-order harmonic sources are emerging as a promising alternative for experiments requiring high flux of ultrashort extreme ultraviolet (XUV) radiation. In this article we describe a versatile experimental setup delivering XUV photons in the 10–50 eV range. The use of cascaded high-order harmonic generation enables us to reach 1.9 mW of average power at 18 eV. Several spectral selection schemes are presented, to isolate a single high-harmonic or a group of them. In the perspective of circular dichroism experiments, we produce highly elliptical XUV radiation using resonant elliptical high-harmonic generation, and circularly polarized XUV by bichromatic bicircular high-harmonic generation. As an illustration of the capacities of the beamline, we focus the XUV beam in a coincidence electron–ion imaging spectrometer, where we measure the photoelectron momentum angular distributions of xenon monomers and dimers.
Variance reduction for Langevin Monte Carlo in high dimensional sampling problems

Sampling from a log-concave distribution function is one core problem that has wide applications in Bayesian statistics and machine learning. While most gradient free methods have slow convergence rate, the Langevin Monte Carlo (LMC) that provides fast convergence requires the computation of gradients. In practice one uses finite-differencing approximations as surrogates, and the method is expensive in high-dimensions. A natural strategy to reduce computational cost in each iteration is to utilize random gradient approximations, such as random coordinate descent (RCD) or simultaneous perturbation stochastic approximation (SPSA). We show by a counterexample that blindly applying RCD does not achieve the goal in the most general setting. The high variance induced by the randomness means a larger number of iterations are needed, and this balances out the saving in each iteration. We then introduce a new variance reduction approach, termed Randomized Coordinates Averaging Descent (RCAD), and incorporate it with both overdamped and underdamped LMC. The methods are termed RCAD-O-LMC and RCAD-U-LMC respectively. The methods still sit in the random gradient approximation framework, and thus the computational cost in each iteration is low. However, by employing RCAD, the variance is reduced, so the methods converge within the same number of iterations as the classical overdamped and underdamped LMC. This leads to a computational saving overall.
Phylogenetic relationships within the genus Wiseana (Lepidoptera: Hepialidae)

Abstract The genus Wiseana Viette, part of the ’Oxycanus’ lineage s. str. (Dugdale 1994), is well known to systematists and applied scientists in New Zealand because it has proved difficult to establish the number and distribution of species, and to find characters to identify the species causing damage. We estimated the phylogenetic relationships within Wiseana using nucleotide sequence data from the ITS2 region of the nuclear, ribosomal DNA. Relationships between Wiseana signata haplotypes and W. umbraculata were resolved using the ITS2 region, and morphological and mtDNA data sets. Other relationships within the genus could not be completely resolved using ITS2, or other data sets. The advantages of combining independently derived Wiseana data sets is discussed.
Dynamical Masses for the Pleiades Binary System HII-2147

We report our long-term spectroscopic monitoring of the Pleiades member HII-2147, which was previously spatially resolved at radio wavelengths in very long baseline interferometry (VLBI) observations. It has also been claimed to be a (presumably short-period) double-lined spectroscopic binary with relatively sharp lines, although no orbit has ever been published. Examination of our new spectroscopic material and the historical radial velocities shows that the current and previous spectra are best interpreted as featuring only a single set of lines of a moderately rapidly rotating star with slowly variable radial velocity, which is one of the sources detected by VLBI. We combine our own and other velocities with the VLBI measurements and new adaptive optics observations to derive the first astrometric-spectroscopic orbit of the G5 + G9 pair, with a period of 18.18 ± 0.11 yr. We infer dynamical masses of 0.897 ± 0.022 M☉ for the spectroscopically visible star and 0.978 ± 0.024 M☉ for the other, along with a distance of pc. The lack of detection of the lines of the more massive component in our spectra can be adequately explained if it is rotating much more rapidly than the star we see. This is consistent with the observation that the lines of the secondary are shallower than expected for a star of its spectral type.