Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning

Recent work has demonstrated the effectiveness of formulating decision making as a supervised learning problem on offline-collected trajectories. However, the benefits of performing sequence modeling on trajectory data is not yet clear. In this work we investigate if sequence modeling has the capability to condense trajectories into useful representations that can contribute to policy learning. To achieve this, we adopt a two-stage framework that first summarizes trajectories with sequence modeling techniques, and then employs these representations to learn a policy along with a desired goal. This design allows many existing supervised offline RL methods to be considered as specific instances of our framework. Within this framework, we introduce Goal-Conditioned Predicitve Coding (GCPC), an approach that brings powerful trajectory representations and leads to performant policies. We conduct extensive empirical evaluations on AntMaze, FrankaKitchen and Locomotion environments, and observe that sequence modeling has a significant impact on some decision making tasks. In addition, we demonstrate that GCPC learns a goal-conditioned latent representation about the future, which serves as an "implicit planner", and enables competitive performance on all three benchmarks

An epigenetic switch induced by Shh signalling regulates gene activation during development and medulloblastoma growth

The Sonic hedgehog (Shh) signalling pathway plays important roles during development and in cancer. Here we report a Shh-induced epigenetic switch that cooperates with Gli to control transcription outcomes. Before induction, poised Shh target genes are marked by a bivalent chromatin domain containing a repressive histone H3K27me3 mark and an active H3K4me3 mark. Shh activation induces a local switch of epigenetic cofactors from the H3K27 methyltransferase polycomb repressive complex 2 (PRC2) to an H3K27me3 demethylase Jmjd3/Kdm6b-centred coactivator complex. We also find that non-enzymatic activities of Jmjd3 are important and that Jmjd3 recruits the Set1/MLL H3K4 methyltransferase complexes in a Shh-dependent manner to resolve the bivalent domain. In vivo, changes of the bivalent domain accompanied Shh-activated cerebellar progenitor proliferation. Overall, our results reveal a regulatory mechanism that underlies the activation of Shh target genes and provides insight into the causes of various diseases and cancers exhibiting altered Shh signalling

Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years

Thermal Decomposition Properties of Epoxy Resin in SF6/N2 Mixture

As a promising alternative for pure SF6, the mixture of SF6/N2 appears to be more economic and environment-friendly on the premise of maintaining similar dielectric properties with pure SF6. But less attention has been paid to the thermal properties of an SF6/N2 mixture, especially with insulation materials overheating happening simultaneously. In this paper, thermal decomposition properties of epoxy resin in SF6/N2 mixture with different SF6 volume rates were studied, and the concentrations of characteristic decomposition components were detected based on concentrations change of some characteristic gas components such as CO2, SO2, H2S, SOF2, and CF4. The results showed that thermal properties of 20% SF6/N2 (volume fraction of SF6 is 20%) mixture has faster degradation than 40% SF6/N2 mixture. As ratio of SF6 content decreases, thermal stability of the system decreases, and the decomposition process of SF6 is exacerbated. Moreover, a mathematical model was established to determine happening of partial overheating faults on the epoxy resin surface in SF6/N2 mixture. Also thermal decomposition process of epoxy resin was simulated by the ReaxFF force field to reveal basic chemical reactions in terms of bond-breaking order, which further verified that CO2 and H2O produced during thermal decomposition of epoxy resin can intensify degradation of SF6 dielectric property

Magnetic Resonance Perfusion-Weighted Imaging in Predicting Hemorrhagic Transformation of Acute Ischemic Stroke: A Retrospective Study

Hemorrhagic transformation (HT) is one of the common complications in patients with acute ischemic stroke (AIS). This study aims to investigate the value of different thresholds of Tmax generated from perfusion-weighted MR imaging (PWI) and the apparent diffusion coefficient (ADC) value in the prediction of HT in AIS. A total of 156 AIS patients were enrolled in this study, with 55 patients in the HT group and 101 patients in non-HT group. The clinical baseline data and multi-parametric MRI findings were compared between HT and non-HT groups to identify indicators related to HT. The optimal parameters for predicting HT and the corresponding cutoff values were obtained using the receiver operating characteristic curve analysis of the volumes of ADC −6 mm2/s and Tmax > 6 s, 8 s, and 10 s. The results showed that the volumes of ADC −6 mm2/s and Tmax > 6 s, 8 s, and 10 s in the HT group were all significantly larger than that in the non-HT group and were all independent risk factors for HT. Early measurement of the volume of Tmax > 10 s had the highest value, with a cutoff lesion volume of 10.5 mL

Effectiveness Analysis and Temperature Effect Mechanism on Chemical and Electrical-Based Transformer Insulation Diagnostic Parameters Obtained from PDC Data

The dielectric monitoring/diagnostic tool, such as Polarization and Depolarization Current (PDC) measurement, is now being widely applied to obtain the status of deteriorated transformers around the world. Nowadays, several works have reported that the chemical and electrical-based transformer insulation diagnostic parameters (absorption ratio, polarization index, paper conductivity, oil conductivity, insulation resistance, etc.) can be easily calculated from the PDC data. It is a fact that before using these parameters to obtain the status of deteriorated transformers, the power engineers should prudently investigate the effectiveness of these parameters. However, there are few papers that investigate the important issue. In addition, the understanding of temperature effect mechanism on these parameters should also be prudently studied. In the present work, we firstly prepare several oil-impregnated pressboard specimens with various insulation statuses by using a sequence of thermal ageing and moisture absorption experiments launched in the laboratory, and then the PDC measurement is performed to obtain the chemical and electrical-based transformer insulation diagnostic parameters. Finally, we systematically interpret the effectiveness and temperature effect mechanism on these chemical and electrical-based transformer insulation diagnostic parameters

Stomach virtual non-enhanced CT with second-generation, dual-energy CT: a preliminary study.

To compare the true non-enhanced (TNE) and virtual non-enhanced (VNE) data sets in patients who underwent gastric preoperative dual-energy CT (DECT) and to evaluate potential radiation dose reduction by omitting a TNE scan.A total of 74 patients underwent gastric DECT. The mean CT values, length, image quality and effective radiation doses for VNE and TNE images were compared.There was no statistical difference in maximal thickness of gastric tumors and maximal diameter of enlarged lymph nodes among the TNE and VNE images (P>0.05). The mean CT value differences between TNE and VNE were statistically significant for all tissue types, except for aorta attenuation measurements (P<0.05), but the absolute differences were under 10 HU. Lower noise was found for VNE images than TNE images (P<0.01). Image quality of VNE was diagnostic but lower than that of TNE (P<0.01). The dose reduction achieved by omitting the TNE acquisition was 21.40 ± 4.44%.VNE scan may potentially replace TNE as part of a multi-phase gastric preoperative staging imaging protocol with consequent saving in radiation dose

Chromatin remodeling factor Brg1 supports the early maintenance and late responsiveness of nestin-lineage adult neural stem and progenitor cells

Insights from embryonic development suggest chromatin remodeling is important in adult neural stem cells (aNSCs) maintenance and self‐renewal, but this concept has not been fully explored in the adult brain. To assess the role of chromatin remodeling in adult neurogenesis, we inducibly deleted Brg1—the core subunit of SWI/SNF‐like Brg1/Brm‐associated factor chromatin remodeling complexes—in nestin‐expressing aNSCs and their progeny in vivo and in culture. This resulted in abnormal adult neurogenesis in the hippocampus, which initially reduced hippocampal aNSCs and progenitor maintenance, and later reduced its responsiveness to physiological stimulation. Mechanistically, deletion of Brg1 appeared to impair cell cycle progression, which is partially due to elevated p53 pathway and p21 expression. Knockdown of p53 rescued the neurosphere growth defects caused by Brg1 deletion. Our results show that epigenetic chromatin remodeling (via a Brg1 and p53/p21‐dependent process) determines the aNSCs and progenitor maintenance and responsiveness of neurogenesis