Auto-Encoding Adversarial Imitation Learning

Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imitation Learning (AIL) sheds light on automatic policy acquisition without access to the reward signal from the environment. In this work, we propose Auto-Encoding Adversarial Imitation Learning (AEAIL), a robust and scalable AIL framework. To induce expert policies from demonstrations, AEAIL utilizes the reconstruction error of an auto-encoder as a reward signal, which provides more information for optimizing policies than the prior discriminator-based ones. Subsequently, we use the derived objective functions to train the auto-encoder and the agent policy. Experiments show that our AEAIL performs superior compared to state-of-the-art methods on both state and image based environments. More importantly, AEAIL shows much better robustness when the expert demonstrations are noisy.Comment: 15 page

Semantic-Aware Fine-Grained Correspondence

Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is implicitly available through a rich set of image-level self-supervised methods. We further design a pixel-level self-supervised learning objective which specifically targets fine-grained correspondence. For downstream tasks, we fuse these two kinds of complementary correspondence representations together, demonstrating that they boost performance synergistically. Our method surpasses previous state-of-the-art self-supervised methods using convolutional networks on a variety of visual correspondence tasks, including video object segmentation, human pose tracking, and human part tracking.Comment: 26 page

Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA).The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame- or object-level visual representations. In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature,we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and design a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on language priors and always neglect visual-language interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all the state-of-the-art models on the challenging NExT-QA benchmark, which demonstrates the effectiveness of the proposed method

An Unstructured Phylogeographic Pattern with Extensive Gene Flow in an Endemic Bird of South China: Collared Finchbill (Spizixos semitorques)

Recent phylogeographical studies indicated that glacial oscillations played a key role on the phylogeographic pattern of extant species. As most studies have previously been carried out on heavily ice-covered regions, such as in European and North American regions, potential effects of climatic oscillations on species that are distributed on ice-free regions are less known. To address this, we investigated the phylogeographic pattern of an avian species endemic to South China, which was not glaciated during the Pleistocene glaciations. By using 2142 bp mitochondrial DNA, we identified 89 haplotypes defined by 39 polymorphic sites. A combination of high haplotype diversity (0.786–1.00) and low nucleotide diversity (0.00132–0.00252) was detected among geographic populations. Explicit genetic divergence was observed between S. s. semitorques and S. s. cinereicapillus but not detected among geographic populations of S. s. semitorques. Divergence time of the two subspecies was dated back to 87 Kyr which is congruent with the interglacial MIS 5. A weak phylogeographic structure due to strong gene flow among geographic populations was identified in this species, suggesting complex topography of South China has not formed barriers for this species

Effects of feed intake restriction during late pregnancy on the function, anti-oxidation capability and acute phase protein synthesis of ovine liver

Objective An experiment was conducted to investigate the effects of feed intake restriction during late pregnancy on the function, anti-oxidation capability and acute phase protein synthesis of ovine liver. Methods Eighteen time-mated ewes with singleton fetuses were allocated to three groups: restricted group 1 (RG1, 0.18 MJ ME/kg W0.75 d, n = 6), restricted group 2 (RG2, 0.33 MJ ME/kg W0.75 d), n = 6) and a control group (CG, ad libitum, 0.67 MJ ME/kg W0.75 d, n = 6). The feed restriction period was from 90 days to 140 days of pregnancy. Results The ewe’s body weight, liver weights, water, and protein content of liver in the restricted groups were reduced compared with the CG group (p0.05). Conclusion The fat accumulation, increased hepatic fibrosis, antioxidant imbalance and modified synthesis of acute phase proteins were induced in ewe’s liver by maternal malnutrition during late pregnancy, which were detrimental for liver function to accommodate pregnancy

An Allosteric-Probe for Detection of Alkaline Phosphatase Activity and Its Application in Immunoassay

A fluorescence strategy for alkaline phosphatase (ALP) assay in complicated samples with high sensitivity and strong stability is developed based on an allosteric probe (AP). This probe consists of two DNA strands, a streptavidin (SA) aptamer labeled by fluorophore and its totally complementary DNA (cDNA) with a phosphate group on the 5′ end. Upon ALP introduction, the phosphate group on the cDNA is hydrolyzed, leaving the unhydrolyzed cDNA sequence for lambda exonuclease (λ exo) digestion and releasing SA aptamer for binding to SA beads, which results in fluorescence enhancement of SA beads that can be detected by flow cytometry or microscopy. We have achieved a detection limit of 0.012 U/mL with a detection range of 0.02~0.15 U/mL in buffer and human serum. These figures of merit are better than or comparable to those of other methods. Because the fluorescence signal is localized on the beads, they can be separated to remove fluorescence background from complicated biological systems. Notably, the new strategy not only applies to ALP detection with simple design, easy operation, high sensitivity, and good compatibility in complex solution, but also can be utilized in ALP-linked immunosorbent assays for the detection of a wide range of targets

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data