Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition

Open-set image recognition is a challenging topic in computer vision. Most of the existing works in literature focus on learning more discriminative features from the input images, however, they are usually insensitive to the high- or low-frequency components in features, resulting in a decreasing performance on fine-grained image recognition. To address this problem, we propose a Complementary Frequency-varying Awareness Network that could better capture both high-frequency and low-frequency information, called CFAN. The proposed CFAN consists of three sequential modules: (i) a feature extraction module is introduced for learning preliminary features from the input images; (ii) a frequency-varying filtering module is designed to separate out both high- and low-frequency components from the preliminary features in the frequency domain via a frequency-adjustable filter; (iii) a complementary temporal aggregation module is designed for aggregating the high- and low-frequency components via two Long Short-Term Memory networks into discriminative features. Based on CFAN, we further propose an open-set fine-grained image recognition method, called CFAN-OSFGR, which learns image features via CFAN and classifies them via a linear classifier. Experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that CFAN-OSFGR performs significantly better than 9 state-of-the-art methods in most cases

Recursive Counterfactual Deconfounding for Object Recognition

Image recognition is a classic and common task in the computer vision field, which has been widely applied in the past decade. Most existing methods in literature aim to learn discriminative features from labeled images for classification, however, they generally neglect confounders that infiltrate into the learned features, resulting in low performances for discriminating test images. To address this problem, we propose a Recursive Counterfactual Deconfounding model for object recognition in both closed-set and open-set scenarios based on counterfactual analysis, called RCD. The proposed model consists of a factual graph and a counterfactual graph, where the relationships among image features, model predictions, and confounders are built and updated recursively for learning more discriminative features. It performs in a recursive manner so that subtler counterfactual features could be learned and eliminated progressively, and both the discriminability and generalization of the proposed model could be improved accordingly. In addition, a negative correlation constraint is designed for alleviating the negative effects of the counterfactual features further at the model training stage. Extensive experimental results on both closed-set recognition task and open-set recognition task demonstrate that the proposed RCD model performs better than 11 state-of-the-art baselines significantly in most cases

Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition

Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate attention maps for distinguishing different categories of fine-grained images. To address this problem, motivated by the temporal attention mechanism in brains, we propose a spatial-temporal attention network for learning fine-grained feature representations, called STAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively. The proposed STAN consists of four modules: a self-attention backbone module for learning a sequence of features with self-attention operations, a spatial feature self-organizing module for facilitating the model training, a spatial-temporal feature learning module for aggregating the re-organized features via a Long Short-Term Memory network, and a context-aware module that is implemented as the forget block of the spatial-temporal feature learning module for preserving/forgetting the long-term memory by utilizing contextual information. Then, we propose a STAN-based method for open-set fine-grained recognition by integrating the proposed STAN network with a linear classifier, called STAN-OSFGR. Extensive experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that the proposed STAN-OSFGR outperforms 9 state-of-the-art open-set recognition methods significantly in most cases

Lower critical solution temperature (LCST) phase behaviour of an ionic liquid and its control by supramolecular host–guest interactions

Lower critical solution temperature (LCST) phase behaviour of an imidazolium- based ionic liquid is reported, which can be controlled by concentration, the choice of cation, anion and solvent, and by supramolecular host–guest complex formation. Molecular dynamics simulations provide insight into the molecular basis of this LCST phenomenon. This thermo-responsive system has potential applications in cloud point extraction processes

Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin By Multi-Temporal SAR Data

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multi-temporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multi-temporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (pre-flood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and an SHPS-based multi-temporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.This work was financially supported by the National Natural Science Foundation of China (grant numbers 41904001 and 41774006), the China Postdoctoral Science Foundation (grant number 2018M640733), the National Key Research and Development Program of China (grant number 2019YFC1509201), and the National Postdoctoral Program for Innovative Talents (grant number BX20180220)

Pressure-Modulated Structural and Magnetic Phase Transitions in Two-Dimensional FeTe: Tetragonal and Hexagonal Polymorphs

Two-dimensional (2D) Fe-chalcogenides with rich structures, magnetisms and superconductivities are highly desirable to reveal the torturous transition mechanism and explore their potential applications in spintronics and nanoelectronics. Hydrostatic pressure can effectively stimulate novel phase transitions between various ordered states and to plot the seductive phase diagram. Herein, the structural evolution and transport characteristics of 2D FeTe were systematically investigated under extreme conditions through comparing two distinct symmetries, i.e., tetragonal (t-) and hexagonal (h-) FeTe. We found that 2D t-FeTe presented the pressure-induced transition from antiferromagnetic to ferromagnetic states at ~ 3 GPa, corresponding to the tetragonal collapse of layered structure. Contrarily, ferromagnetic order of 2D h-FeTe was retained up to 15 GPa, evidently confirmed by electrical transport and Raman measurements. Furthermore, the detailed P-T phase diagrams of both 2D t-FeTe and h-FeTe were mapped out with the delicate critical conditions. We believe our results can provide a unique platform to elaborate the extraordinary physical properties of Fe-chalcogenides and further to develop their practical applications.Comment: 22 Pages, 5 Figure

exoplanet : gradient-based probabilistic inference for exoplanet data & other astronomical time series

Funding: This research was partially conducted during the Exostar19 program at the Kavli Institute for Theoretical Physics at UC Santa Barbara, which was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958."exoplanet" is a toolkit for probabilistic modeling of astronomical time series data, with a focus on observations of exoplanets, using PyMC3 (Salvatier et al., 2016). PyMC3 is a flexible and high-performance model-building language and inference engine that scales well to problems with a large number of parameters. "exoplanet" extends PyMC3's modeling language to support many of the custom functions and probability distributions required when fitting exoplanet datasets or other astronomical time series. While it has been used for other applications, such as the study of stellar variability, the primary purpose of "exoplanet" is the characterization of exoplanets or multiple star systems using time-series photometry, astrometry, and/or radial velocity. In particular, the typical use case would be to use one or more of these datasets to place constraints on the physical and orbital parameters of the system, such as planet mass or orbital period, while simultaneously taking into account the effects of stellar variability.Publisher PDFPeer reviewe

A JWST NIRSpec Phase Curve for WASP-121b: Dayside Emission Strongest Eastward of the Substellar Point and Nightside Conditions Conducive to Cloud Formation

We present the first exoplanet phase curve measurement made with the JWST NIRSpec instrument, highlighting the exceptional stability of this newly-commissioned observatory for exoplanet climate studies. The target, WASP-121b, is an ultrahot Jupiter with an orbital period of 30.6 hr. We analyze two broadband light curves generated for the NRS1 and NRS2 detectors, covering wavelength ranges of 2.70-3.72 micron and 3.82-5.15 micron, respectively. Both light curves exhibit minimal systematics, with approximately linear drifts in the baseline flux level of 30 ppm/hr (NRS1) and 10 ppm/hr (NRS2). Assuming a simple brightness map for the planet described by a low-order spherical harmonic dipole, our light curve fits suggest that the phase curve peaks coincide with orbital phases $3.36 \pm 0.11$ deg (NRS1) and $2.66 \pm 0.12$ deg (NRS2) prior to mid-eclipse. This is consistent with the strongest dayside emission emanating from eastward of the substellar point. We measure planet-to-star emission ratios of $3,924 \pm 7$ ppm (NRS1) and $4,924 \pm 9$ ppm (NRS2) for the dayside hemisphere, and $136 \pm 8$ ppm (NRS1) and $630 \pm 10$ ppm (NRS2) for the nightside hemisphere. The latter nightside emission ratios translate to planetary brightness temperatures of $926 \pm 12$ K (NRS1) and $1,122 \pm 10$ K (NRS2), which are low enough for a wide range of refractory condensates to form, including enstatite and forsterite. A nightside cloud deck may be blocking emission from deeper, hotter layers of the atmosphere, potentially helping to explain why cloud-free 3D general circulation model simulations systematically over-predict the nightside emission for WASP-121b.Comment: Accepted for publication in Astrophysical Journal Letters on December 29, 202

Low Cell-Matrix Adhesion Reveals Two Subtypes of Human Pluripotent Stem Cells.

We show that a human pluripotent stem cell (hPSC) population cultured on a low-adhesion substrate developed two hPSC subtypes with different colony morphologies: flat and domed. Notably, the dome-like cells showed higher active proliferation capacity and increased several pluripotent genes' expression compared with the flat monolayer cells. We further demonstrated that cell-matrix adhesion mediates the interaction between cell morphology and expression of KLF4 and KLF5 through a serum response factor (SRF)-based regulatory double loop. Our results provide a mechanistic view on the coupling among adhesion, stem cell morphology, and pluripotency, shedding light on the critical role of cell-matrix adhesion in the induction and maintenance of hPSC