Seer: Language Instructed Video Prediction with Latent Diffusion Models

Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frames. It is a highly challenging task to ground task-level goals specified by instructions and high-fidelity frames together, requiring large-scale data and computation. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We inflate the denoising U-Net and language conditioning model with two novel techniques, Autoregressive Spatial-Temporal Attention and Frame Sequential Text Decomposer, to propagate the rich prior knowledge in the pretrained T2I models across the frames. With the well-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2) and Bridgedata datasets demonstrate our superior video prediction performance with around 210-hour training on 4 RTX 3090 GPUs: decreasing the FVD of the current SOTA model from 290 to 200 on SSv2 and achieving at least 70\% preference in the human evaluation.Comment: 17 pages, 15 figure

Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance

Recently, people have shown that large-scale pre-training from internet-scale data is the key to building generalist models, as witnessed in NLP. To build embodied generalist agents, we and many other researchers hypothesize that such foundation prior is also an indispensable component. However, it is unclear what is the proper concrete form to represent those embodied foundation priors and how they should be used in the downstream task. In this paper, we propose an intuitive and effective set of embodied priors that consist of foundation policy, value, and success reward. The proposed priors are based on the goal-conditioned MDP. To verify their effectiveness, we instantiate an actor-critic method assisted by the priors, called Foundation Actor-Critic (FAC). We name our framework as Foundation Reinforcement Learning (FRL), since it completely relies on embodied foundation priors to explore, learn and reinforce. The benefits of FRL are threefold. (1) Sample efficient. With foundation priors, FAC learns significantly faster than traditional RL. Our evaluation on the Meta-World has proved that FAC can achieve 100% success rates for 7/8 tasks under less than 200k frames, which outperforms the baseline method with careful manual-designed rewards under 1M frames. (2) Robust to noisy priors. Our method tolerates the unavoidable noise in embodied foundation models. We show that FAC works well even under heavy noise or quantization errors. (3) Minimal human intervention: FAC completely learns from the foundation priors, without the need of human-specified dense reward, or providing teleoperated demos. Thus, FAC can be easily scaled up. We believe our FRL framework could enable the future robot to autonomously explore and learn without human intervention in the physical world. In summary, our proposed FRL is a novel and powerful learning paradigm, towards achieving embodied generalist agents

Optimized coded prefetching scheme in hierarchical cache-enabled networks

Caching popular content at small base stations (SBSs) of a wireless edge network is a good choice to reduce user request latency and backhaul load. However, an effective coded caching scheme to solve the challenges in hierarchical cacheenabled networks (HCENs), including the limited SBS cache capacity, rich content amount, especially the deployment of coded prefetching, has not yet been fully studied. In this paper, we propose a caching scheme based on coded prefetching and coded transmission in HCENs. In particular, considering the impact of preference of users, the SBSs cache capacity, and the number of contents of coded linear combination, we design a cache probability matrix, where contents is stored at each SBS by linear combination with caching probability. Furthermore, the proposed algorithm including prefetching and transmission is analyzed, the expression of the system average delay is derived, and an optimization problem of minimizing the average delay is established to obtain the optimal cache probability matrix. Finally, the simulation results verify the effectiveness of our coded prefetching caching scheme, which can achieve lower average delay than other caching schemes with uncoded prefetching and coded transmission in HCENs

Regulatory mechanism of CaMKII δ mediated by RIPK3 on myocardial fibrosis and reversal effects of RIPK3 inhibitor GSK'872

Background: Myocardial fibrosis (MF) remains a prominent challenge in heart disease. The role of receptor-interacting protein kinase 3 (RIPK3)-mediated necroptosis is evident in the pathogenesis of numerous heart diseases. Concurrently, the activation of Ca2+/calmodulin-dependent protein kinase (CaMKII) is pivotal in cardiovascular disease (CVD). This study aimed to evaluate the impact and underlying mechanisms of RIPK3 on myocardial injury in MF and to elucidate the potential involvement of CaMKII. Methods: Building upon our previous research methods [1], wild-type (WT) mice and RIPK3 knockout (RIPK3 -/-) mice underwent random assignment for transverse aortic constriction (TAC) in vivo. Four weeks post-procedure, the MF model was effectively established. Parameters such as the extent of MF, myocardial injury, RIPK3 expression, necroptosis, CaMKII activity, phosphorylation of mixed lineage kinase domain-like protein (MLKL), mitochondrial ultrastructural details, and oxidative stress levels were examined. Cardiomyocyte fibrosis was simulated in vitro using angiotensin II on cardiac fibroblasts. Results: TAC reliably produced MF, myocardial injury, CaMKII activation, and necroptosis in mice. RIPK3 depletion ameliorated these conditions. The RIPK3 inhibitor, GSK'872, suppressed the expression of RIPK3 in myocardial fibroblasts, leading to improved fibrosis and inflammation, diminished CaMKII oxidation and phosphorylation levels, and the rectification of CaMKIIδ alternative splicing anomalies. Furthermore, GSK'872 downregulated the expressions of RIPK1, RIPK3, and MLKL phosphorylation, attenuated necroptosis, and bolstered the oxidative stress response. Conclusions: Our data suggested that in MF mice, necroptosis was augmented in a RIPK3-dependent fashion. There seemed to be a positive correlation between CaMKII activation and RIPK3 expression. The adverse effects on myocardial fibrosis mediated by CaMKII δ through RIPK3 could potentially be mitigated by the RIPK3 inhibitor, GSK'872. This offered a fresh perspective on the amelioration and treatment of MF and myocardial injury