Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

A Net Energy Analysis of the Global Agriculture, Aquaculture, Fishing and Forestry System

The global agriculture, aquaculture, fishing and forestry (AAFF) energy system is subject to three unsustainable trends: (1) the approaching biophysical limits of AAFF; (2) the role of AAFF as a driver of environmental degradation; and (3) the long-term declining energy efficiency of AAFF due to growing dependence on fossil fuels. In response, we conduct a net energy analysis for the period 1971–2017 and review existing studies to investigate the global AAFF energy system and its vulnerability to the three unsustainable trends from an energetic perspective. We estimate the global AAFF system represents 27.9% of societies energy supply in 2017, with food energy representing 20.8% of societies total energy supply. We find that the net energy-return-on-investment (net EROI) of global AAFF increased from 2.87:1 in 1971 to 4.05:1 in 2017. We suggest that rising net EROI values are being fuelled in part by ‘depleting natures accumulated energy stocks’. We also find that the net energy balance of AAFF increased by 130% in this period, with at the same time a decrease in both the proportion of rural residents and also the proportion of the total population working in AAFF—which decreased from 19.8 to 10.3%. However, this comes at the cost of growing fossil fuel dependency which increased from 43.6 to 62.2%. Given the increasing probability of near-term fossil fuel scarcity, the growing impacts of climate change and environmental degradation, and the approaching biophysical limits of global AAFF, ‘Odum’s hoax’ is likely soon to be revealed

Telling the collective story? Moroccan-Dutch young adults’ negotiation of a collective identity through storytelling

Researchers taking a social constructionist perspective on identity agree that identities are constructed and negotiated in interaction. However, empirical studies in this field are often based on interviewer–interviewee interaction or focus on interactions with members of a socially dominant out-group. How identities are negotiated in interaction with in-group members remains understudied. In this article we use a narrative approach to study identity negotiation among Moroccan-Dutch young adults, who constitute both an ethnic and a religious (Muslim) minority in the Netherlands. Our analysis focuses on the topics that appear in focus group participants’ stories and on participants’ responses to each other’s stories. We find that Moroccan-Dutch young adults collectively narrate their experiences in Dutch society in terms of discrimination and injustice. Firmly grounded in media discourse and popular wisdom, a collective narrative of a disadvantaged minority identity emerges. However, we also find that this identity is not uncontested. We use the concept of second stories to explain how participants negotiate their collective identity by alternating stories in which the collective experience of deprivation is reaffirmed with stories in which challenging or new evaluations of the collective experience are offered. In particular, participants narrate their personal experiences to challenge recurring evaluations of discrimination and injustice. A new collective narrative emerges from this work of joint storytelling

Impact of Systemic Inflammation and Autoimmune Diseases on apoA-I and HDL Plasma Levels and Functions

The cholesterol of high-density lipoproteins (HDLs) and its major proteic component, apoA-I, have been widely investigated as potential predictors of acute cardiovascular (CV) events. In particular, HDL cholesterol levels were shown to be inversely and independently associated with the risk of acute CV diseases in different patient populations, including autoimmune and chronic inflammatory disorders. Some relevant and direct anti-inflammatory activities of HDL have been also recently identified targeting both immune and vascular cell subsets. These studies recently highlighted the improvement of HDL function (instead of circulating levels) as a promising treatment strategy to reduce inflammation and associated CV risk in several diseases, such as systemic lupus erythematosus and rheumatoid arthritis. In these diseases, anti-inflammatory treatments targeting HDL function might improve both disease activity and CV risk. In this narrative review, we will focus on the pathophysiological relevance of HDL and apoA-I levels/functions in different acute and chronic inflammatory pathophysiological conditions

A diagnostic algorithm for the atherogenic apolipoprotein B dyslipoproteinemias.

Item does not contain fulltextGiven the high prevalence of the atherogenic forms of apolipoprotein B (apoB) dyslipoproteinemias and the effectiveness of appropriate therapy, we feel that access for all physicians to simple, effective diagnostic aids that enable use of widely available technology is vitally important in clinical lipidology. In this Review, therefore, we present a diagnostic algorithm for the diagnosis of these disorders that is based on concentrations of total cholesterol, triglyceride and apoB. By including apoB values, lipoprotein number and composition can be deduced and each of the classic dyslipoproteinemias identified. All three parameters can be accurately and inexpensively determined in clinical laboratories and, therefore, this algorithm can be used by any physician to make an accurate diagnosis without use of specialist research laboratories. Just as the application of LDL cholesterol measurement moved clinical practice forward from plasma lipids to lipoprotein lipids, we believe that the use of apoB will further advance diagnosis and treatment of dyslipoproteinemias