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Large-scale high throughput metabolomic technologies are indispensable components of systems biology in terms of discovering and defining the metabolite parts of the system. However, the lack of a plant metabolite spectral library limits the metabolite identification of plant metabolomic studies. Here, we have created a plant metabolite spectral library using 544 authentic standards, which increased the efficiency of identification for untargeted metabolomic studies. The process of creating the spectral library was described, and the mzVault library was deposited in the public repository for free download. Furthermore, based on the spectral library, we describe a process of creating a pseudo-targeted method, which was applied to a proof-of-concept study of Arabidopsis leaf extracts. As authentic standards become available, more metabolite spectra can be easily incorporated into the spectral library to improve the mzVault package. 

The gravitational microlensing technique is most sensitive to planets in a Jupiter-like orbit and has detected more than 200 planets. However, only a few wide-orbit (s> 2) microlensing planets have been discovered, wheresis the planet-to-host separation normalized to the angular Einstein ring radius,θ_E. Here, we present the discovery and analysis of a strong candidate wide-orbit microlensing planet in the event OGLE-2017-BLG-0448. The whole light curve exhibits long-term residuals to the static binary-lens single-source model, so we investigate the residuals by adding the microlensing parallax, microlensing xallarap, an additional lens, or an additional source. For the first time, we observe a complex degeneracy between all four effects. The wide-orbit models withs∼ 2.5 and a planet-to-host mass ratio ofq∼ 10⁻⁴are significantly preferred, but we cannot rule out the close models withs∼ 0.35 andq∼ 10⁻³. A Bayesian analysis based on a Galactic model indicates that, despite the complicated degeneracy, the surviving wide-orbit models all contain a super-Earth-mass to Neptune-mass planet at a projected planet-host separation of ∼6 au and the surviving close-orbit models all consist of a Jovian-mass planet at ∼1 au. The host star is probably an M or K dwarf. We discuss the implications of this dimension-degeneracy disaster on microlensing light-curve analysis and its potential impact on statistical studies.

 

Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design. By producing features that are guaranteed to transform covariantly to the group actions on the inputs, group-equivariant convolutional neural networks (G-CNNs) achieve significantly improved generalization performance in learning tasks with intrinsic symmetry. General theory and practical implementation of G-CNNs have been studied for planar images under either rotation or scaling transformation, but only individually. We present, in this paper, a roto-scale-translation equivariant CNN (RST -CNN), that is guaranteed to achieve equivariance jointly over these three groups via coupled group convolutions. Moreover, as symmetry transformations in reality are rarely perfect and typically subject to input deformation, we provide a stability analysis of the equivariance of representation to input distortion, which motivates the truncated expansion of the convolutional filters under (pre-fixed) low-frequency spatial modes. The resulting model provably achieves deformation-robust RST equivariance, i.e., the RST symmetry is still “approximately” preserved when the transformation is “contaminated” by a nuisance data deformation, a property that is especially important for out-of-distribution generalization. Numerical experiments on MNIST, Fashion-MNIST, and STL-10 demonstrate that the proposed model yields remarkable gains over prior arts, especially in the small data regime where both rotation and scaling variations are present within the data. 

Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translation-equivariant (ST-equivariant) CNN with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve equivariance for the regular representation of the scaling-translation group ST. To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation, a property which is theoretically analyzed and empirically verified. Numerical experiments demonstrate that the proposed scaling-translation-equivariant network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size. 

Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translation-equivariant (ST-equivariant) CNN with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve equivariance for the regular representation of the scaling-translation group ST. To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation, a property which is theoretically analyzed and empirically verified. Numerical experiments demonstrate that the proposed scaling-translation-equivariant network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size. 

Aims. We inspect the four microlensing events KMT-2021-BLG-1968, KMT-2021-BLG-2010, KMT-2022-BLG-0371, and KMT-2022-BLG-1013, for which the light curves exhibit partially covered short-term central anomalies. We conduct detailed analyses of the events with the aim of revealing the nature of the anomalies. Methods. We tested various models that can explain the anomalies of the individual events, including the binary-lens (2L1S) and binary-source (1L2S) interpretations. Under the 2L1S interpretation, we thoroughly inspected the parameter space to determine the existence of degenerate solutions, and if they existed, we tested whether the degeneracy could be resolved. Results. We find that the anomalies in KMT-2021-BLG-2010 and KMT-2022-BLG-1013 are uniquely defined by planetary-lens interpretations with planet-to-host mass ratios of q ~ 2.8 × 10 −3 and ~1.6 × 10 −3 , respectively. For KMT-2022-BLG-0371, a planetary solution with a mass ratio q ~ 4 × 10 −4 is strongly favored over the other three degenerate 2L1S solutions with different mass ratios based on the χ 2 and relative proper motion arguments, and a 1L2S solution is clearly ruled out. For KMT-2021-BLG-1968, on the other hand, we find that the anomaly can be explained either by a planetary or a binary-source interpretation, making it difficult to firmly identify the nature of the anomaly. From the Bayesian analyses of the identified planetary events, we estimate that the masses of the planet and host are ( M p / M J , M h / M ⊙ ) = (1.07 −0.68 +1.15 , 0.37 −0.23 +0.40 ), (0.26 −0.11 +0.13 , 0.63 −0.28 +0.32 ), and (0.31 −0.16 +0.46 , 0.18 −0.10 +0.28 ) for KMT-2021-BLG-2010L, KMT-2022-BLG-0371L, and KMT-2022-BLG-1013L, respectively.