Neural Categorical Priors for Physics-Based Character Control

Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in this paradigm for controlling physics-based characters with significantly improved motion quality and diversity over existing state-of-the-art methods. The proposed method uses reinforcement learning (RL) to initially track and imitate life-like movements from unstructured motion clips using the discrete information bottleneck, as adopted in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure compresses the most relevant information from the motion clips into a compact yet informative latent space, i.e., a discrete space over vector quantized codes. By sampling codes in the space from a trained categorical prior distribution, high-quality life-like behaviors can be generated, similar to the usage of VQ-VAE in computer vision. Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting. To address the issue, we further propose a technique named prior shifting to adjust the prior distribution using curiosity-driven RL. The outcome distribution is demonstrated to offer sufficient behavioral diversity and significantly facilitates upper-level policy learning for downstream tasks. We conduct comprehensive experiments using humanoid characters on two challenging downstream tasks, sword-shield striking and two-player boxing game. Our results demonstrate that the proposed framework is capable of controlling the character to perform considerably high-quality movements in terms of behavioral strategies, diversity, and realism. Videos, codes, and data are available at https://tencent-roboticsx.github.io/NCP/

Aesthetic Enhancement via Color Area and Location Awareness

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring

Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Summarizing knowledge from animals and human beings inspires robotic innovations. In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments. Inspired by large pre-trained models witnessed with impressive performance in language and image understanding, we introduce the power of advanced deep generative models to produce motor control signals stimulating legged robots to act like real animals. Unlike conventional controllers and end-to-end RL methods that are task-specific, we propose to pre-train generative models over animal motion datasets to preserve expressive knowledge of animal behavior. The pre-trained model holds sufficient primitive-level knowledge yet is environment-agnostic. It is then reused for a successive stage of learning to align with the environments by traversing a number of challenging obstacles that are rarely considered in previous approaches, including creeping through narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc. Finally, a task-specific controller is trained to solve complex downstream tasks by reusing the knowledge from previous stages. Enriching the knowledge regarding each stage does not affect the usage of other levels of knowledge. This flexible framework offers the possibility of continual knowledge accumulation at different levels. We successfully apply the trained multi-level controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles, and play in a designed challenging multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the robots. The present research pushes the frontier of robot control with new insights on reusing multi-level pre-trained knowledge and solving highly complex downstream tasks in the real world

Hippo Signaling Suppresses Cell Ploidy and Tumorigenesis through Skp2

大多数真核生物的体细胞是二倍体，即仅含有两组染色体，分别遗传自父本和母本。而一些特定组织如心脏、肝脏等就含有多倍体细胞，特别是肝脏组织含有较高比例的四、八倍体等多倍体细胞。肝脏是人体的重要解毒器官，同时酒精、肝炎病毒等毒性物质或毒性代谢物容易诱发肝细胞的基因突变，多倍体被认为有利于提供代偿性的正常基因来维持肝脏稳态。然而肝脏受损后，多倍体细胞将会受胁迫进行增殖，再生修复受损的肝组织。因此研究机体调控多倍体细胞产生及多倍体细胞进行细胞分裂的调控机理对于理解肝癌的发病机理和肝癌的治疗至关重要。Hippo信号通路在调节组织成体干细胞的分化和增殖，调控器官再生与尺寸大小中具有重要作用。深入研究发现, Hippo信号通路下游效应分子YAP通过AKT-SKP2信号促进二倍体细胞向多倍体转化及多倍体细胞的生长增殖。本项研究阐明了Hippo缺失及YAP激活促进多倍体细胞产生及增殖作为肝癌发生发展中的一个重要机制，为肝癌诊疗提供了新的策略。 周大旺，博士，厦门大学生命科学学院教授、副院长、国家杰出青年基金获得者。【Abstract】Polyploidy can lead to aneuploidy and tumorigenesis. Here, we report that the Hippo pathway effector Yap promotes the diploid-polyploid conversion and polyploid cell growth through the Akt-Skp2 axis. Yap strongly induces the acetyltransferase p300-mediated acetylation of the E3 ligase Skp2 via Akt signaling. Acetylated Skp2 is exclusively localized to the cytosol, which causes hyper-accumulation of the cyclin-dependent kinase inhibitor p27, leading to mitotic arrest and subsequently cell polyploidy. In addition, the pro-apoptotic factors FoxO1/3 are overly degraded by acetylated Skp2, resulting in polyploid cell division, genomic instability, and oncogenesis. importantly, the depletion or inactivation of Akt or Skp2 abrogated Hippo signal deficiency-induced liver tumorigenesis, indicating their epistatic interaction. Thus, we conclude that Hippo-Yap signaling suppresses cell polyploidy and oncogenesis through Skp2.该研究工作获得了国家自然科学基金委、国家重点基础研究发展计划(973)项目、青年千人计划和中央高校基本科研基金的资助。 The Yap (S127A) transgenic mice were kindly provided by Dr. Fernando Camargo from Harvard Medical School, Boston, MA. D.Z. and L.C. were supported by the National Natural Science Foundation of China (31625010,U1505224, and J1310027 to D.Z.; 81422018, U1405225, and 81372617 to L.C.; 81472229 to L.H.), the National Basic Research Program (973) of China (2015CB910502 to L.C.), the Fundamental Research Funds for the Central Universities of China-Xiamen University (20720140551 to L.C. and 2013121034 and 20720140537 to D.Z.)

Sensor placement for lifetime maximization in monitoring oil pipelines

ABSTRACT Wireless sensor networks (WSNs) have been widely deployed and it is crucial to properly control the energy consumption of the sensor nodes to achieve the maximum WSNs' operation time (i.e., lifetime) as they are normally battery powered. In this paper, for sensor nodes that are utilized to monitor oil pipelines, we study the linear sensor placement problem with the goal of maximizing their lifetime. For a simple equal-distance placement scheme, we first illustrate that the result based on the widely used ideal power model can be misleading (i.e., adding more sensor nodes can improve WSN's lifetime) when compared to that of a realistic power model derived from Tmote Sky sensors. Then, we study equal-power placement schemes and formulate the problem as a MILP (mixed integer linear programming) problem. In addition, two efficient placement heuristics are proposed. The evaluation results show that, even with the Tmote power model, the equal-power placement schemes can improve the WSN's lifetime by up to 29% with properly selected number of sensor nodes, the distance between them and the corresponding transmission power levels. Moreover, one heuristic scheme actually obtains almost the same results as that of MILP, which is optimal. The real deployment in one oil field is also discussed

IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation

Judging how an image is visually appealing is a complicated and subjective task. This highly motivates having a machine learning model to automatically evaluate image aesthetic by matching the aesthetics of general public. Although deep learning methods have been successfully learning good visual features from images, correctly assessing image aesthetic quality is still challenging for deep learning. To tackle this, we propose a novel multi-view convolutional neural network to assess image aesthetic by analyzing image color composition and space formation (IAACS). Specifically, from different views of an image, including its key color components with their contributions, the image space formation and the image itself, our network extracts their corresponding features through our proposed feature extraction module (FET) and the ImageNet weight-based classification model. By fusing the extracted features, our network produces an accurate prediction score distribution of image aesthetic. Experiment results have shown that we have achieved a superior performance

Nitric oxide synthase-mediated early nitric oxide burst alleviates water stress-induced oxidative damage in ammonium-supplied rice roots

Abstract Background Nutrition with ammonium (NH4 +) can enhance the drought tolerance of rice seedlings in comparison to nutrition with nitrate (NO3 −). However, there are still no detailed studies investigating the response of nitric oxide (NO) to the different nitrogen nutrition and water regimes. To study the intrinsic mechanism underpinning this relationship, the time-dependent production of NO and its protective role in the antioxidant defense system of NH4 +- or NO3 −-supplied rice seedlings were studied under water stress. Results An early NO burst was induced by 3 h of water stress in the roots of seedlings subjected to NH4 + treatment, but this phenomenon was not observed under NO3 − treatment. Root oxidative damage induced by water stress was significantly higher for treatment with NO3 − than with NH4 + due to reactive oxygen species (ROS) accumulation in the former. Inducing NO production by applying the NO donor 3 h after NO3 − treatment alleviated the oxidative damage, while inhibiting the early NO burst by applying the NO scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (c-PTIO) increased root oxidative damage in NH4 + treatment. Application of the nitric oxide synthase (NOS) inhibitor N(G)-nitro-L-arginine methyl ester(L-NAME) completely suppressed NO synthesis in roots 3 h after NH4 + treatment and aggravated water stress-induced oxidative damage. Therefore, the aggravation of oxidative damage by L-NAME might have resulted from changes in the NOS-mediated early NO burst. Water stress also increased the activity of root antioxidant enzymes (catalase, superoxide dismutase, and ascorbate peroxidase). These were further induced by the NO donor but repressed by the NO scavenger and NOS inhibitor in NH4 +-treated roots. Conclusion These findings demonstrate that the NOS-mediated early NO burst plays an important role in alleviating oxidative damage induced by water stress by enhancing the antioxidant defenses in roots supplemented with NH4 +

Elevational Variation in Soil Amino Acid and Inorganic Nitrogen Concentrations in Taibai Mountain, China.

Amino acids are important sources of soil organic nitrogen (N), which is essential for plant nutrition, but detailed information about which amino acids predominant and whether amino acid composition varies with elevation is lacking. In this study, we hypothesized that the concentrations of amino acids in soil would increase and their composition would vary along the elevational gradient of Taibai Mountain, as plant-derived organic matter accumulated and N mineralization and microbial immobilization of amino acids slowed with reduced soil temperature. Results showed that the concentrations of soil extractable total N, extractable organic N and amino acids significantly increased with elevation due to the accumulation of soil organic matter and the greater N content. Soil extractable organic N concentration was significantly greater than that of the extractable inorganic N (NO3--N + NH4+-N). On average, soil adsorbed amino acid concentration was approximately 5-fold greater than that of the free amino acids, which indicates that adsorbed amino acids extracted with the strong salt solution likely represent a potential source for the replenishment of free amino acids. We found no appreciable evidence to suggest that amino acids with simple molecular structure were dominant at low elevations, whereas amino acids with high molecular weight and complex aromatic structure dominated the high elevations. Across the elevational gradient, the amino acid pool was dominated by alanine, aspartic acid, glycine, glutamic acid, histidine, serine and threonine. These seven amino acids accounted for approximately 68.9% of the total hydrolyzable amino acid pool. The proportions of isoleucine, tyrosine and methionine varied with elevation, while soil major amino acid composition (including alanine, arginine, aspartic acid, glycine, histidine, leucine, phenylalanine, serine, threonine and valine) did not vary appreciably with elevation (p>0.10). The compositional similarity of many amino acids across the elevational gradient suggests that soil amino acids likely originate from a common source or through similar biochemical processes