Meta Reinforcement Learning with Latent Variable Gaussian Processes

Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a model-based reinforcement learning setting and show that our meta-learning model effectively generalizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines.Comment: 11 pages, 7 figure

Chiral Symmetry Breaking out of QCD Effective Locality

The QCD non-perturbative property of Effective Locality whose essential meaning has been disclosed recently, is here questioned about the chiral symmetry breaking phenomenon, one of the two major issues of the non-perturbative phase of QCD. As a first attempt, quenching and the eikonal approximation are used so as to simplify calculations which are quite involved. Chiral symmetry breaking appears to be realised in close connection to the Effective Locality mass scale, $\mu^2$ , as could be expected.Comment: ICNAAM 2018, Analysis of Quantum Field Theory IV Conference extended abstrac

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Recent methods of image analysis in remote sensing lack a sufficient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal effects, different locations, atmospheric conditions, or even a slightly different sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents’ behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm

Treatment of patients with comorbid depression and diabetes with metformin and milnacipran

Depression is twice as frequent in patients with diabetes as in the general population, and has a negative impact on self-care, adherence to treatment, and the general prognosis of diabetes. This underscores the importance of screening all diabetic patients for depression and, if necessary, treating it with an effective antidepressant drug in parallel with standard diabetes treatment. In a recent study, a simple two-question screening tool was used to screen diabetic patients for comorbid depression. The effects of the serotonin and norepinephrine reuptake inhibitor antidepressant, milnacipran, on metabolic parameters and depressive symptoms in 64 diabetic patients with comorbid depression detected by this screen were studied. Patients received milnacipran for 6 months, in addition to standard diabetes treatment with metformin. At the end of the study, 72% of patients had responded to antidepressant treatment (≥50% reduction of baseline Beck Depression Score). The proportion of patients with <8% glycosylated hemoglobin HbA1c (a common indication in diabetes of the need for intensive therapeutic intervention) had decreased significantly from 46.6% at baseline to 6.9%. HbA1c, fasting blood glucose, body mass index, total and low-density lipoprotein cholesterol, and serum triglyceride levels were all significantly decreased in patients with an antidepressant response, but not in patients whose depressive symptoms had not responded to milnacipran

Dolomitization of the Hatch Hill arenites and the Burden Iron Ore

The Taconic allochthon is a sequence of Cambrian or Precambrian to Ordovician rocks. It is composed of predominantly deep water argillaceous and subordinate arenaceous and calcareous rocks that were deposited on the continental rise and slope. During the Ordovician, sediments which were earlier deposited in the slope-rise environment were incorporated into the accretionary prism of an island arc that approached from the east, and subsequently overthrust the carbonate platform. The Hatch Hill Formation is part of the Taconic sequence. It consists of dominantly black-gray slates, minor amounts of sandstones and carbonates. Previous workers have recognized the presence of dolomite and a siderite ore (the Burden Iron Ore) in these sandstones. The stratigraphic position of the siderite ore was not clear prior to this study. This study showed that the Burden Iron Ore is the basal part of the Hatch Hill Formation in the area studied, based on comparison with the northern Taconic lithologic stratigraphy. It conformably overlies the Bomoseen Formation. The contact between the Bomoseen Formation and the Hatch Hill Formation is marked by a disconformity that has not been noted elsewhere in the Taconics. The origin of the iron ore is closely related to the origin of the dolomite of the Hatch Hill Formation. It can be demonstrated that both phases occur as cements that formed after the deposition of the Hatch Hill arenites. The cements formed as a by-product of the decay and fermentation of organic matter that was probably deposited in the black-gray shales of the Hatch Hill Formation. Isotopic evidence and geochemical considerations show that the siderite cements formed after sulfate reduction was completed and that the development of dolomite cements most likely took place in the lower part of the zone of methanogenesis. Paleotemperatures determined from oxygen isotope analyses indicate that the dolomite cements probably formed at a temperature of approximately 750C, if the pore fluid was not affected by meteoric or brine waters. This would imply a depth of formation of 2-3 km, if present day geothermal gradients for a passive continental margin sequence are assumed. The formation of dolomite therefore took place during or after the deposition of the Pawlet Formation (flysch sequence)