406 research outputs found
BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations
Real-world planning problems\unicode{x2014}including autonomous driving and
sustainable energy applications like carbon storage and resource
exploration\unicode{x2014}have recently been modeled as partially observable
Markov decision processes (POMDPs) and solved using approximate methods. To
solve high-dimensional POMDPs in practice, state-of-the-art methods use online
planning with problem-specific heuristics to reduce planning horizons and make
the problems tractable. Algorithms that learn approximations to replace
heuristics have recently found success in large-scale problems in the fully
observable domain. The key insight is the combination of online Monte Carlo
tree search with offline neural network approximations of the optimal policy
and value function. In this work, we bring this insight to partially observed
domains and propose BetaZero, a belief-state planning algorithm for POMDPs.
BetaZero learns offline approximations based on accurate belief models to
enable online decision making in long-horizon problems. We address several
challenges inherent in large-scale partially observable domains; namely
challenges of transitioning in stochastic environments, prioritizing action
branching with limited search budget, and representing beliefs as input to the
network. We apply BetaZero to various well-established benchmark POMDPs found
in the literature. As a real-world case study, we test BetaZero on the
high-dimensional geological problem of critical mineral exploration.
Experiments show that BetaZero outperforms state-of-the-art POMDP solvers on a
variety of tasks.Comment: 20 page
Changing the script: a typology of Dutch theatre manuscripts in the Southern Low Countries, and the interaction between manuscript and print (Seventeenth–Eighteenth centuries)
NWO016.Veni.195.371Medieval and Early Modern Studie
Reconstruction of three-dimensional porous media using generative adversarial neural networks
To evaluate the variability of multi-phase flow properties of porous media at
the pore scale, it is necessary to acquire a number of representative samples
of the void-solid structure. While modern x-ray computer tomography has made it
possible to extract three-dimensional images of the pore space, assessment of
the variability in the inherent material properties is often experimentally not
feasible. We present a novel method to reconstruct the solid-void structure of
porous media by applying a generative neural network that allows an implicit
description of the probability distribution represented by three-dimensional
image datasets. We show, by using an adversarial learning approach for neural
networks, that this method of unsupervised learning is able to generate
representative samples of porous media that honor their statistics. We
successfully compare measures of pore morphology, such as the Euler
characteristic, two-point statistics and directional single-phase permeability
of synthetic realizations with the calculated properties of a bead pack, Berea
sandstone, and Ketton limestone. Results show that GANs can be used to
reconstruct high-resolution three-dimensional images of porous media at
different scales that are representative of the morphology of the images used
to train the neural network. The fully convolutional nature of the trained
neural network allows the generation of large samples while maintaining
computational efficiency. Compared to classical stochastic methods of image
reconstruction, the implicit representation of the learned data distribution
can be stored and reused to generate multiple realizations of the pore
structure very rapidly.Comment: 21 pages, 20 figure
Optimizing Carbon Storage Operations for Long-Term Safety
To combat global warming and mitigate the risks associated with climate
change, carbon capture and storage (CCS) has emerged as a crucial technology.
However, safely sequestering CO2 in geological formations for long-term storage
presents several challenges. In this study, we address these issues by modeling
the decision-making process for carbon storage operations as a partially
observable Markov decision process (POMDP). We solve the POMDP using belief
state planning to optimize injector and monitoring well locations, with the
goal of maximizing stored CO2 while maintaining safety. Empirical results in
simulation demonstrate that our approach is effective in ensuring safe
long-term carbon storage operations. We showcase the flexibility of our
approach by introducing three different monitoring strategies and examining
their impact on decision quality. Additionally, we introduce a neural network
surrogate model for the POMDP decision-making process to handle the complex
dynamics of the multi-phase flow. We also investigate the effects of different
fidelity levels of the surrogate model on decision qualities
Antitumour and antiangiogenic effects of Aplidin® in the 5TMM syngeneic models of multiple myeloma
Aplidin® is an antitumour drug, currently undergoing phase II evaluation in different haematological and solid tumours. In this study, we analysed the antimyeloma effects of Aplidin in the syngeneic 5T33MM model, which is representable for the human disease. In vitro, Aplidin inhibited 5T33MMvv DNA synthesis with an IC50 of 3.87 nM. On cell-cycle progression, the drug induced an arrest in transition from G0/G1 to S phase, while Western blot showed a decreased cyclin D1 and CDK4 expression. Furthermore, Aplidin induced apoptosis by lowering the mitochondrial membrane potential, by inducing cytochrome c release and by activating caspase-9 and caspase-3. For the in vivo experiment, 5T33MM-injected C57Bl/KaLwRij mice were intraperitoneally treated with vehicle or Aplidin (90 μg kg−1 daily). Chronic treatment with Aplidin was well tolerated and reduced serum paraprotein concentration by 42% (P<0.001), while BM invasion with myeloma cells was decreased by 35% (P<0.001). Aplidin also reduced the myeloma-associated angiogenesis to basal values. This antiangiogenic effect was confirmed in vitro and explained by inhibition of endothelial cell proliferation and vessel formation. These data indicate that Aplidin is well tolerated in vivo and its antitumour and antiangiogenic effects support the use of the drug in multiple myeloma
Extramedullary disease in multiple myeloma: a systematic literature review
Extramedullary involvement (or extramedullary disease, EMD) represents an aggressive form of multiple myeloma (MM), characterized by the ability of a clone and/or subclone to thrive and grow independent of the bone marrow microenvironment. Several different definitions of EMD have been used in the published literature. We advocate that true EMD is restricted to soft-tissue plasmacytomas that arise due to hematogenous spread and have no contact with bony structures. Typical sites of EMD vary according to the phase of MM. At diagnosis, EMD is typically found in skin and soft tissues; at relapse, typical sites involved include liver, kidneys, lymph nodes, central nervous system (CNS), breast, pleura, and pericardium. The reported incidence of EMD varies considerably, and differences in diagnostic approach between studies are likely to contribute to this variability. In patients with newly diagnosed MM, the reported incidence ranges from 0.5% to 4.8%, while in relapsed/refractory MM the reported incidence is 3.4 to 14%. Available data demonstrate that the prognosis is poor, and considerably worse than for MM without soft-tissue plasmacytomas. Among patients with plasmacytomas, those with EMD have poorer outcomes than those with paraskeletal involvement. CNS involvement is rare, but prognosis is even more dismal than for EMD in other locations, particularly if there is leptomeningeal involvement. Available data on treatment outcomes for EMD are derived almost entirely from retrospective studies. Some agents and combinations have shown a degree of efficacy but, as would be expected, this is less than in MM patients with no extramedullary involvement. The paucity of prospective studies makes it difficult to justify strong recommendations for any treatment approach. Prospective data from patients with clearly defined EMD are important for the optimal evaluation of treatment outcomes
Extramedullary disease in multiple myeloma: a systematic literature review
Extramedullary involvement (or extramedullary disease, EMD) represents an aggressive form of multiple myeloma (MM), characterized by the ability of a clone and/or subclone to thrive and grow independent of the bone marrow microenvironment. Several different definitions of EMD have been used in the published literature. We advocate that true EMD is restricted to soft-tissue plasmacytomas that arise due to hematogenous spread and have no contact with bony structures. Typical sites of EMD vary according to the phase of MM. At diagnosis, EMD is typically found in skin and soft tissues; at relapse, typical sites involved include liver, kidneys, lymph nodes, central nervous system (CNS), breast, pleura, and pericardium. The reported incidence of EMD varies considerably, and differences in diagnostic approach between studies are likely to contribute to this variability. In patients with newly diagnosed MM, the reported incidence ranges from 0.5% to 4.8%, while in relapsed/refractory MM the reported incidence is 3.4 to 14%. Available data demonstrate that the prognosis is poor, and considerably worse than for MM without soft-tissue plasmacytomas. Among patients with plasmacytomas, those with EMD have poorer outcomes than those with paraskeletal involvement. CNS involvement is rare, but prognosis is even more dismal than for EMD in other locations, particularly if there is leptomeningeal involvement. Available data on treatment outcomes for EMD are derived almost entirely from retrospective studies. Some agents and combinations have shown a degree of efficacy but, as would be expected, this is less than in MM patients with no extramedullary involvement. The paucity of prospective studies makes it difficult to justify strong recommendations for any treatment approach. Prospective data from patients with clearly defined EMD are important for the optimal evaluation of treatment outcomes
MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings
Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up
A pattern-search-based inverse method
Uncertainty in model predictions is caused to a large extent by the uncertainty in model parameters, while the identification of model parameters is demanding because of the inherent heterogeneity of the aquifer. A variety of inverse methods has been proposed for parameter identification. In this paper we present a novel inverse method to constrain the model parameters (hydraulic conductivities) to the observed state data (hydraulic heads). In the method proposed we build a conditioning pattern consisting of simulated model parameters and observed flow data. The unknown parameter values are simulated by pattern searching through an ensemble of realizations rather than optimizing an objective function. The model parameters do not necessarily follow a multi-Gaussian distribution, and the nonlinear relationship between the parameter and the response is captured by the multipoint pattern matching. The algorithm is evaluated in two synthetic bimodal aquifers. The proposed method is able to reproduce the main structure of the reference fields, and the performance of the updated model in predicting flow and transport is improved compared with that of the prior model.The authors gratefully acknowledge the financial support from the Ministry of Science and Innovation, project CGL2011-23295. The first author also acknowledges the scholarship provided by the China Scholarship Council (CSC [2007] 3020). The authors would like to thank Gregoire Mariethoz (University of New South Wales) and Philippe Renard (University of Neuchatel) for their enthusiastic help in answering questions about the direct sampling algorithm. Gregoire Mariethoz and two anonymous reviewers are also thanked for their comments during the reviewing process, which helped improving the final paper.Zhou ., H.; Gómez-Hernández, JJ.; Li ., L. (2012). A pattern-search-based inverse method. 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