Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation

In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional knowledge generated by pretext tasks can further improve the performance of few-shot learning (FSL) as it differs from human-annotated supervision (i.e., class labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks. A hierarchical tree constructing component and a gated selection aggregating component is introduced to construct the tree structure and find richer transferable knowledge that can rapidly adapt to novel classes with a few labeled images. Extensive experiments show that our HTS can significantly enhance multiple few-shot methods to achieve new state-of-the-art performance on four benchmark datasets. The code is available at: https://github.com/remiMZ/HTS-ECCV22.Comment: 22 pages, 9 figures and 4 tables Accepted by ECCV 202

Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning

Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A popular solution to the problem is to infer task identity as augmented state using a context-based encoder, for which efficient learning of robust task representations remains an open challenge. In this work, we provably improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives, to robustify task representation learning against sparse reward and distribution shift. Theoretical analysis and experiments are presented to demonstrate the superior performance and robustness of our end-to-end and model-free framework compared to prior algorithms across multiple meta-RL benchmarks.Comment: 21 pages, 7 figure

Association between visceral fat area and diabetic retinopathy among people with type 2 diabetes mellitus: a cross-sectional study in Ningbo, Zhejiang Province, China

AimThe objective of this study is to investigate the relationship between visceral fat area (VFA) and diabetic retinopathy (DR) in the context of type 2 diabetes mellitus (T2DM) within Ningbo, China.MethodsThe data of a total of 3,707 subjects with T2DM treated at The First Affiliated Hospital of Ningbo University were enrolled. The existence and severity of diabetic retinopathy were assessed by employing the 45° two-field stereoscopic digital photography. Subjects were categorized into four distinct groups: those without DR (NDR), individuals with mild non-proliferative DR (mild NPDR), people with moderate non-proliferative DR (moderate NPDR), and those suffering from vision-threatening DR (VTDR). Bio-electrical impedance was employed to estimate the Visceral fat area (VFA). Multinomial logistic regression models were utilized to evaluate the association between VFA and DR.ResultsThe mean VFA in patients without diabetic retinopathy (NDR) was notably lower compared to that of patients with diabetic retinopathy (DR) (85.21 ± 37.78 vs. 97.37 ± 44.58 cm2, p < 0.001). As the severity of DR increased, VFA increased gradually but insignificantly (94.41 ± 43.13 cm2, 96.75 ± 40.82 cm2, 100.84 ± 49.34 cm2, p = 0.294). After adjusting the confounding factors, there was an association identified between VFA and the occurrence of DR (OR = 1.020, 95% CI = 1.016–1.024). It showed that regardless of BMI, whether it’s less than 25 kg/m2 or greater than or equal to 25 kg/m2, a higher VFA (≥100 cm2) level came with a higher prevalence of DR (p < 0.001).ConclusionThe outcomes of this research indicate a modest association between VFA and the incidence of DR among Chinese patients who have been diagnosed with T2DM in Ningbo

Dynamics of Associative Polymers with High Density of Reversible Bonds

We design and synthesize unentangled associative polymers carrying unprecedented high fractions of stickers, up to eight per Kuhn segment, that can form strong pairwise hydrogen bonding of $\sim20k_BT$ without microphase separation. The reversible bonds significantly slow down the polymer dynamics but nearly do not change the shape of linear viscoelastic spectra. Moreover, the structural relaxation time of associative polymers increases exponentially with the fraction of stickers and exhibits a universal yet non-Arrhenius dependence on the distance from polymer glass transition temperature. These results cannot be understood within the framework of the classic sticky-Rouse model but are rationalized by a renormalized Rouse model, which highlights an unexpected influence of reversible bonds on the structural relaxation rather than the shape of viscoelastic spectra for associative polymers with high concentrations of stickers.Comment: 4 figure

Internacionalização do Ensino Superior: Um estudo de caso na Universidade de Brasília – UnB

A internacionalização ganha destaque no cenário acadêmico e as Instituições de Ensino Superior devem se adequar a esses novos parâmetros, uma exigência comum do mercado e da população. O objetivo deste artigo é avaliar o processo de internacionalização da Universidade de Brasília – UnB. A abordagem metodológica foi a pesquisa exploratória, em função das informações levantadas sobre o processo de internacionalização da UnB. Foram utilizados modelos e indicadores de avaliação propostos na literatura. Os resultados indicam que se verifica o processo de internacionalização na UnB em foco, o qual atende parte dos requisitos estabelecidos pelo modelo de Knight. Esse estudo permite inferir que seu processo de internacionalização encontra-se em fase avançada, com capacidade para avanços significativos em um curto período

Retrieval of Chemical Abundances in Titan's Upper Atmosphere from Cassini UVIS Observations with Pointing Motion

Cassini/UVIS FUV observations of stellar occultations at Titan are well suited for probing its atmospheric composition and structure. However, due to instrument pointing motion, only five out of tens of observations have been analyzed. We present an innovative retrieval method that corrects for the effect of pointing motion by forward modeling the Cassini/UVIS instrument response function with the pointing motion value obtained from the SPICE C‐kernel along the spectral dimension. To illustrate the methodology, an occultation observation made during flyby T52 is analyzed, when the Cassini spacecraft had insufficient attitude control. A high‐resolution stellar model and an instrument response simulator that includes the position of the point source on the detector are used for the analysis of the pointing motion. The Markov Chain Monte‐Carlo method is used to retrieve the line‐of‐sight abundance profiles of eleven species (CH_4, C_2H_2, C_2H_4, C_2H_6, C_4H_2, C_6H_6, HCN, C_2N_2, HC_3N, C_6N_2 and haze particles) in the spectral vector fitting process. We obtain tight constraints on all of the species aside from C_2H_6, C_2N_2 and C_6N_2, for which we only retrieved upper limits. This is the first time that the T52 occultation was used to derive abundances of major hydrocarbon and nitrile species in Titan's upper and middle atmosphere, as pointing motion prohibited prior analysis. With this new method, nearly all of the occultations obtained over the entire Cassini mission could yield reliable profiles of atmospheric composition, allowing exploration of Titan's upper atmosphere over seasons, latitudes, and longitudes

Earth as an Exoplanet: A Two-dimensional Alien Map

Resolving spatially varying exoplanet features from single-point light curves is essential for determining whether Earth-like worlds harbor geological features and/or climate systems that influence habitability. To evaluate the feasibility and requirements of this spatial-feature resolving problem, we present an analysis of multi-wavelength single-point light curves of Earth, where it plays the role of a proxy exoplanet. Here, ~10,000 Deep Space Climate Observatory/Earth Polychromatic Imaging Camera frames collected over a two-year period were integrated over the Earth's disk to yield a spectrally dependent point source and analyzed using singular value decomposition. We found that, between the two dominant principal components (PCs), the second PC contains surface-related features of the planet, while the first PC mainly includes cloud information. We present the first two-dimensional (2D) surface map of Earth reconstructed from light curve observations without any assumptions of its spectral properties. This study serves as a baseline for reconstructing the surface features of Earth-like exoplanets in the future

Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images

Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods. © 2020 IEEE.National Science FoundationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]