The European Union and the United Nations Convention on the Law of the Sea

This Essay comments on EU participation in UNCLOS and its implementation. It addresses first the nature of the EU as a contracting party and outlines the modalities for its participation. It then reviews the international implementation of the UNCLOS obligations and the implementation/status of the Convention under EU law

Direction of arrival estimation using robust complex Lasso

The Lasso (Least Absolute Shrinkage and Selection Operator) has been a popular technique for simultaneous linear regression estimation and variable selection. In this paper, we propose a new novel approach for robust Lasso that follows the spirit of M-estimation. We define $M$-Lasso estimates of regression and scale as solutions to generalized zero subgradient equations. Another unique feature of this paper is that we consider complex-valued measurements and regression parameters, which requires careful mathematical characterization of the problem. An explicit and efficient algorithm for computing the $M$-Lasso solution is proposed that has comparable computational complexity as state-of-the-art algorithm for computing the Lasso solution. Usefulness of the $M$-Lasso method is illustrated for direction-of-arrival (DoA) estimation with sensor arrays in a single snapshot case.Comment: Paper has appeared in the Proceedings of the 10th European Conference on Antennas and Propagation (EuCAP'2016), Davos, Switzerland, April 10-15, 201

Multichannel sparse recovery of complex-valued signals using Huber's criterion

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. This requires careful characterization of robust complex-valued loss functions as well as Huber's criterion function for the multivariate sparse regression problem. We devise a greedy algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible performance loss compared to SNIHT under Gaussian noise. Usefulness of the method is illustrated in source localization application with sensor arrays.Comment: To appear in CoSeRa'15 (Pisa, Italy, June 16-19, 2015). arXiv admin note: text overlap with arXiv:1502.0244

Nonparametric Simultaneous Sparse Recovery: an Application to Source Localization

We consider multichannel sparse recovery problem where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. Many popular greedy or convex algorithms perform poorly under non-Gaussian heavy-tailed noise conditions or in the face of outliers. In this paper, we propose the usage of mixed $\ell_{p,q}$ norms on data fidelity (residual matrix) term and the conventional $\ell_{0,2}$-norm constraint on the signal matrix to promote row-sparsity. We devise a greedy pursuit algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Simulation studies highlight the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. Usefulness of the methods are illustrated in source localization application with sensor arrays.Comment: Paper appears in Proc. European Signal Processing Conference (EUSIPCO'15), Nice, France, Aug 31 -- Sep 4, 201

On how to achieve reference to covert social constructions

What does it mean to say that some features, such as gender, race and sexual orientation, are socially constructed? Many scholars claim that social constructionism about a kind is a version of realism about that kind, according to which the corresponding kind is a social construction, that it, it is constituted by social factors and practices. Social constructionism, then, is a version of realism about a kind that asserts that the kind is real, and puts forward a particular view about the nature of the kind, namely, that it is constituted by social factors and practices. Social constructivists about human kinds such as gender, race and sexual orientation often make an additional claim, namely, that these kinds are social constructions but they are typically believed to be biological kinds (that is, people are typically wrong about the nature of these kinds). Ron Mallon (2017) calls social constructions that are (falsely) taken to be biological kinds covert social constructions. This paper is about how we could have terms in our natural language that come to refer to covert social constructions

Preaching to the choir? Economic analysis of Church Growth

Economic theory, applied economic modeling and econometric methods offer advantageous tools for analyzing numerous organizations, institutions and social contexts which are not inherently downright economical by nature, as religious markets. In contemporary rational choice religious market models, church growth is assumed to depend on surplus resources available for church development. These extra resources can exist as volunteer work and extra monetary contributions, delivered by enthusiasts and active members, signaling devotion and personal sacrifice. These inputs produce more members and attendants into churches. These hypotheses are tested by applying religious market data from Finland. Models are estimated by comparing data from the dominant state church and the competitive free-church. Both models seem to give support for previous argumentation, emphasizing the importance of volunteer activism and surplus efforts for the church growth.church growth, rational choice, religion, cost-benefit analysis, voluntary work

Structural testing of Business Cycles

In this article, the predictability performance of certain classical business cycle theories are tested against contemporary statistical methods by using Finnish macroeconomic data. Keynesian multiplier- accelerator model derivatives and neo-classical real business cycle models are compared to statistical stochastic time-series methods. Some philosophical considerations on the scientific principles and macroeconomic analysis are extended for applied econometric practice. VAR and SUTSE models are estimated and compared against classical theory implications. It is found that in this case, SUTSE model has a superior forecasting ability and that pure statistical algorithms are the most efficient alternatives for predicting Finnish business cycle data.Business Cycle, Real Business Cycle Theory, VAR, SUTSE, multiplier-acceleration