Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

ORB5: a global electromagnetic gyrokinetic code using the PIC approach in toroidal geometry

This paper presents the current state of the global gyrokinetic code ORB5 as an update of the previous reference [Jolliet et al., Comp. Phys. Commun. 177 409 (2007)]. The ORB5 code solves the electromagnetic Vlasov-Maxwell system of equations using a PIC scheme and also includes collisions and strong flows. The code assumes multiple gyrokinetic ion species at all wavelengths for the polarization density and drift-kinetic electrons. Variants of the physical model can be selected for electrons such as assuming an adiabatic response or a ``hybrid'' model in which passing electrons are assumed adiabatic and trapped electrons are drift-kinetic. A Fourier filter as well as various control variates and noise reduction techniques enable simulations with good signal-to-noise ratios at a limited numerical cost. They are completed with different momentum and zonal flow-conserving heat sources allowing for temperature-gradient and flux-driven simulations. The code, which runs on both CPUs and GPUs, is well benchmarked against other similar codes and analytical predictions, and shows good scalability up to thousands of nodes

Bone marrow-derived endothelial progenitor cells are a major determinant of nascent tumor neovascularization

Tumors build vessels by cooption of pre-existing vasculature and de novo recruitment of bone marrow (BM)-derived endothelial progenitor cells (EPCs). However, the contribution and the functional role of EPCs in tumor neoangiogenesis are controversial. Therefore, by using genetically marked BM progenitor cells, we demonstrate the precise spatial and temporal contribution of EPCs to the neovascularization of three transplanted and one spontaneous breast tumor in vivo using high-resolution microscopy and flow cytometry. We show that early tumors recruit BM-derived EPCs that differentiate into mature BM-derived endothelial cells (ECs) and luminally incorporate into a subset of sprouting tumor neovessels. Notably, in later tumors, these BM-derived vessels are diluted with non-BM-derived vessels from the periphery, which accounts for purported differences in previously published reports. Furthermore, we show that specific ablation of BM-derived EPCs with alpha-particle-emitting anti-VE-cadherin antibody markedly impaired tumor growth associated with reduced vascularization. Our results demonstrate that BM-derived EPCs are critical components of the earliest phases of tumor neoangiogenesis

Amenability of algebras of approximable operators

We give a necessary and sufficient condition for amenability of the Banach algebra of approximable operators on a Banach space. We further investigate the relationship between amenability of this algebra and factorization of operators, strengthening known results and developing new techniques to determine whether or not a given Banach space carries an amenable algebra of approximable operators. Using these techniques, we are able to show, among other things, the non-amenability of the algebra of approximable operators on Tsirelson's space.Comment: 20 pages, to appear in Israel Journal of Mathematic

Progress and challenges to the global waste management system

Rapid economic growth, urbanization and increasing population have caused (materially intensive) resource consumption to increase, and consequently the release of large amounts of waste to the environment. From a global perspective, current waste and resource management lacks a holistic approach covering the whole chain of product design, raw material extraction, production, consumption, recycling and waste management. In this article, progress and different sustainability challenges facing the global waste management system are presented and discussed. The study leads to the conclusion that the current, rather isolated efforts, in different systems for waste management, waste reduction and resource management are indeed not sufficient in a long term sustainability perspective. In the future, to manage resources and wastes sustainably, waste management requires a more systems-oriented approach that addresses the root causes for the problems. A specific issue to address is the development of improved feedback information (statistics) on how waste generation is linked to consumption

Convergence of trust-region methods based on probabilistic models

In this paper we consider the use of probabilistic or random models within a classical trust-region framework for optimization of deterministic smooth general nonlinear functions. Our method and setting differs from many stochastic optimization approaches in two principal ways. Firstly, we assume that the value of the function itself can be computed without noise, in other words, that the function is deterministic. Secondly, we use random models of higher quality than those produced by usual stochastic gradient methods. In particular, a first order model based on random approximation of the gradient is required to provide sufficient quality of approximation with probability greater than or equal to 1/2. This is in contrast with stochastic gradient approaches, where the model is assumed to be "correct" only in expectation. As a result of this particular setting, we are able to prove convergence, with probability one, of a trust-region method which is almost identical to the classical method. Hence we show that a standard optimization framework can be used in cases when models are random and may or may not provide good approximations, as long as "good" models are more likely than "bad" models. Our results are based on the use of properties of martingales. Our motivation comes from using random sample sets and interpolation models in derivative-free optimization. However, our framework is general and can be applied with any source of uncertainty in the model. We discuss various applications for our methods in the paper

The High Radiosensitizing Efficiency of a Trace of Gadolinium-Based Nanoparticles in Tumors

International audienceWe recently developed the synthesis of ultrasmall gadolinium-based nanoparticles (GBN), (hydrodynamic diameter <5 nm) characterized by a safe behavior after intravenous injection (renal clearance, preferential accumulation in tumors). Owing to the presence of gadolinium ions, GBN can be used as contrast agents for magnetic resonance imaging (MRI) and as radiosensitizers. The attempt to determine the most opportune delay between the intravenous injection of GBN and the irradiation showed that a very low content of radiosensitizing nanoparticles in the tumor area is sufficient (0.1 μg/g of particles, i.e. 15 ppb of gadolinium) for an important increase of the therapeutic effect of irradiation. Such a promising and unexpected result is assigned to a suited distribution of GBN within the tumor, as revealed by the X-ray fluorescence (XRF) maps