Bayesian Generational Population-Based Training

Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittleness of RL algorithms to core hyperparameters and network architecture choice. Furthermore, non-stationarities such as evolving training data and increased agent complexity mean that different hyperparameters and architectures may be optimal at different points of training. This motivates AutoRL, a class of methods seeking to automate these design choices. One prominent class of AutoRL methods is Population-Based Training (PBT), which have led to impressive performance in several large scale settings. In this paper, we introduce two new innovations in PBT-style methods. First, we employ trust-region based Bayesian Optimization, enabling full coverage of the high-dimensional mixed hyperparameter search space. Second, we show that using a generational approach, we can also learn both architectures and hyperparameters jointly on-the-fly in a single training run. Leveraging the new highly parallelizable Brax physics engine, we show that these innovations lead to large performance gains, significantly outperforming the tuned baseline while learning entire configurations on the fly. Code is available at https://github.com/xingchenwan/bgpbt.Comment: AutoML Conference 2022. 10 pages, 4 figure, 3 tables (28 pages, 10 figures, 7 tables including references and appendices

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

『大東世語』「紕漏」篇注釈稿

departmental bulletin pape

Environmental DNA Captures Variations in Fish Assemblages with Distance from Dams in Karst Reservoirs

Dam impoundment can cause many adverse impacts on fish assemblages upstream of dams. Here, we investigated fish diversity in one plateau riverine reservoir (Wanfeng Reservoir) using environmental DNA (eDNA) metabarcoding technology. The following conclusions were drawn: (1) 39 species of fish were monitored belonging to 9 orders and 13 families in the Wanfeng Reservoir, most of which were Cypriniformes and included a variety of common farmed fish belonging to Culter, Oreochromis, Acipenser, and Clarias; (2) the fish assemblage structures in the Up (upstream section), Mid (midstream section), and RA (reservoir area section) of this reservoir was significantly different (p-value p-value p-value < 0.05) were the main environmental stressors causing differences in fish assemblages in different sites of the Wanfeng Reservoir. This study concluded that dam construction in the karst region provided habitats for the establishment and dispersal of exotic fish

Frizzled-related proteins 4 (SFRP4) rs1802073G allele predicts the elevated serum lipid levels during acitretin treatment in psoriatic patients from Hunan, China

Background Acitretin is a second-generation synthetic retinoid, and is widely used for treating the severe psoriasis vulgaris. However, it should be chosen with caution for its cardiovascular risk, and it is reported that acitretin may increase the serum lipids. The purpose of this study is to investigate the relationship between the Frizzled-related proteins 4 (SFRP4) rs1802073 polymorphism and the changes of serum lipids in Chinese psoriatic patients during the treatment with acitretin. Methods In our study, 100 psoriatic patients were recruited systematically treated with acitretin (30 mg/day) for at least eight weeks. Data of the patients’ demographic and clinical characteristics and the results of serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were collected pre- and post-treatment. Results A total of 84 psoriatic patients were enrolled and divided into three groups by SFRP4 rs1802073 genotypes. The patients who carried with TT genotype had maintained levels of TG and LDL-C after acitretin treatment, while patients with GG/GT genotypes had significantly elevated levels of serum TG and LDL-C compared to the TT genotype (ΔTG%: 27.53 ± 59.13 vs −1.47 ± 37.79, p = 0.026, ΔLDL-C%: 10.62 ± 26.57 vs −1.29 ± 17.07, p = 0.042). The association of rs1802073 with TG and LDL-C profiles remained significant after adjusting for age, gender, and body mass index. Although without significance, the pre-post change in serum level of TC across rs1802073 GG/GT genotypes demonstrated a trend similar to TG and LDL, and the serum level of HDL-C demonstrated a trend opposite to TG, TC and LDL. Conclusions Our results demonstrated that SFRP4 rs1802073 polymorphism was found to be associated with elevated serum lipid levels after acitretin treatment, and it may serve as a genetic marker of safe and precise treatment for individual psoriatic patients

Brucella Downregulates Tumor Necrosis Factor-α to Promote Intracellular Survival via Omp25 Regulation of Different MicroRNAs in Porcine and Murine Macrophages

Brucella spp. impedes the production of pro-inflammatory cytokines by its outer membrane protein Omp25 in order to promote survival and immune evasion. However, how Omp25 regulates tumor necrosis factor (TNF-α) expression in different mammalian macrophages remains unclear. In this study, we investigated the potential mechanisms by which Omp25 regulates TNF-α expression and found that Omp25-deficient mutant of B. suis exhibited an enhanced TNF-α expression compared with wild-type (WT) B. suis, whereas ectopic expression of Omp25 suppressed LPS-induced TNF-α production at both protein and mRNA levels in porcine alveolar macrophages (PAMs) and murine macrophage RAW264.7 cells. We observed that Omp25 protein as well as WT B. suis upregulated miR-146a, -181a, -181b, and -301a-3p and downregulated TRAF6 and IRAK1 in both PAMs and RAW264.7 cells, but separately upregulates miR-130a-3p in PAMs and miR-351-5p in RAW264.7. The upregulation of miR-146a or miR-351-5p attenuated TNF-α transcription by targeting TRAF6 and IRAK1 at the 3′ untranslated region (UTR), resulting in inhibition of NF-kB pathway, while upregulation of miR-130a-3p, -181a, or -301a-3p correlated temporally with decreased TNF-α by targeting its 3′UTR in PAMs or RAW264.7 cells. In contrast, inhibition of miR-130a-3p, -146a, -181a, and -301a-3p attenuated the inhibitory effects of Omp25 on LPS-induced TNF-α in PAMs, while inhibition of miR-146a, -181a, -301a-3p, and -351-5p attenuated the inhibitory effects of Omp25 in RAW264.7, resulting in an increased TNF-α production and decreased intracellular bacteria in both cells. Taken together, our results demonstrate that Brucella downregulates TNF-α to promote intracellular survival via Omp25 regulation of different microRNAs in porcine and murine macrophages