Risk-averse stochastic dynamic power dispatch based on deep reinforcement learning with risk-oriented Graph-Gan sampling

The increasing penetration of renewable energy sources (RES) brings volatile stochasticity, which significantly challenge the optimal dispatch of power systems. This paper aims at developing a cost-effective and robust policy for stochastic dynamic optimization of power systems, which improves the economy as well as avoiding the risk of high costs in some critical scenarios with small probability. However, it is hard for existing risk-neutral methods to incorporate risk measure since most samples are normal. For this regard, a novel risk-averse policy learning approach based on deep reinforcement learning with risk-oriented sampling is proposed. Firstly, a generative adversarial network (GAN) with graph convolutional neural network (GCN) is proposed to learn from historical data and achieve risk-oriented sampling. Specifically, system state is modelled as graph data and GCN is employed to capture the underlying correlation of the uncertainty corresponding to the system topology. Risk knowledge is the embedded to encourage more critical scenarios are sampled while aligning with historical data distributions. Secondly, a modified deep reinforcement learning (DRL) with risk-measure under soft actor critic framework is proposed to learn the optimal dispatch policy from sampling data. Compared with the traditional deep reinforcement learning which is risk-neutral, the proposed method is more robust and adaptable to uncertainties. Comparative simulations verify the effectiveness of the proposed method

Human-in-the-Loop Schema Induction

Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction(IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.Comment: 10 pages, ACL2023 demo trac

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. In this study, five HCC cohorts with 1059 patients were collected to calculate transcriptional stemness indexes (mRNAsi) by the one-class logistic regression machine learning algorithm. In the TCGA-LIHC cohort, we found mRNAsi was an independent prognostic factor, and 626 mRNAsi-related genes were identified by Spearman correlation analysis. The HCC stemness risk model (HSRM) was trained in the TCGA-LIHC cohort and significantly discriminated overall survival in four independent cohorts. HSRM was also significantly associated with transarterial chemoembolization treatment response and rapid tumor growth in HCC patients. Consensus clustering was conducted based on mRNAsi-related genes to divide 1059 patients into two stemness subtypes. On gene set variation analysis, samples of subtype I were found enriched with pathways such as DNA replication and cell cycle, while several liver-specific metabolic pathways were inhibited in these samples. Somatic mutation analysis revealed more frequent mutations of TP53 and RB1 in the subtype I samples. In silico analysis suggested topoisomerase, cyclin-dependent kinase, and histone deacetylase as potential targets to inhibit HCC stemness. In vitro assay showed two predicted compounds, Aminopurvalanol-a and NCH-51, effectively suppressed oncosphere formation and impaired viability of HCC cell lines, which may shed new light on HCC treatment

Co-Supplementation of Flos Sophorae Extract with Tremella fuciformis Polysaccharides Improves Physicochemical, Textural, Rheological, and Antioxidant Properties of Low-Fat Yogurts

Flos Sophorae extract (FSE) with abundant rutin and Tremella fuciformis polysaccharides (TP) could act as novel natural improvers of low-fat yogurt due to their health benefits and properties of interacting with proteins. This study attempted to understand how FSE or its combination with TP influences physicochemical, textural, and antioxidant properties of low-fat yogurts. The results indicated that the low concentrations of FSE (1.2 or 2.4%) increased the antioxidant activity, prompted the S. thermophilus growth, water holding capacity (WHC), and textural and sensory properties, and shortened the fermentation time, but reduced the rheological properties of yogurts compared with the control. Co-supplementing appropriate TP with the optimum FSE concentration (2.4%) improved the rheological properties, and further enhanced the S. thermophilus growth, WHC, textural and antioxidant properties, and sensory scores of yogurts compared with the 2.4% FSE group, with the best effects at 0.4 mg/mL TP. Taken together, co-supplementation of polyphenols extract FSE with Tremella fuciformis polysaccharides may be an available strategy to optimize health-promoting properties and overcome defects of low-fat yogurts

The Effect of Soybean Peptides on Improving Quality and the ACE Inhibitory Bioactivity of Extruded Rice

It is crucial to address the dietary problems of hypertensive patients. The effect and mechanism of different contents of soybean protein on cooking quality and angiotensin-converting enzyme (ACE) inhibitory action in the extruded rice were firstly investigated. The results showed that the extruded rice with soybean protein possessed the higher taste value (90.32 ± 2.31), hardness (2.65 ± 0.01 g), and good pasting quality (p ≤ 0.05). Meanwhile, the soybean protein notably retarded the starch digestibility; the sample with 6% soybean protein showed the fewest rapidly digestible starch (RDS) content (78.82 ± 0.01 mg g−1) and the most slowly digestible starch (SDS) content (8.97 ± 0.45 mg g−1). Importantly, the ACE inhibition rate improved from 17.09 ± 0.01% to 74.02 ± 0.65% in the 6% soybean protein sample because of the production of peptides. The peptide composition of samples were compared, which showed that the effective ACE-inhibitory peptides usually contain 2~20 amino acids, and Pro, Leu, Ile, Val, Phe, and Ala were the main components. Overall, moderate soybean protein would give a good quality and lower ACE activity in extruded food