Lying idiots: reality television & lie to me

This article addresses perhaps the key question for Television Studies: Who does television think you are? It argues that Reality Television answers this question by producing its viewers as, in the Classical Greek sense, idiots (meaning private and ignorant persons). Idiots are the perfect target for the advertising dollar that supports commercial television production. As Reg Grundy observes, Reality Television is anything but reality. With its tight framings of reality, it paradoxically operates to sever the viewing self from reality—from the “truth” of life. Lie to Me is the type of television we are left with after the demise of Reality Television. Lie to Me makes us self-conscious, in the strongest sense, and thus sustains the mission of Reality Television. By dragging the notion of reality into its self-serving fictions, it puts the viewer into the dangerous position of being unable to lie to television. If Reality Television constrained reality to falsity, Lie to Me implicates the viewer in the zone where falsity transforms into reality. Lastly, this article enquires into the possibilities, in today’s television ecology, for a mode of TV citizenship that would counter the abject viewing position of the consumerist idiot.

A history of the lie of innocence: sons who become orphans

This thesis deals with a history of the lie of innocence, its inception in biblical representation and its development in literary representations from the eighteenth century to contemporary times. The aim is to disclose the way the lie functioned across time both in Christian societies and in the secular ones in their wake.

Suicidal ideation and physical illness: Does the link lie with depression?

Objective
Medical illness is a risk factor for suicidality; however, disorder-specific risks are not well-known and these relationships are often explained by major depressive disorder (MDD). We aimed to investigate the relationship between suicidal ideation, MDD and medical illnesses in an age-stratified, population-based sample of men participating in the Geelong Osteoporosis Study.

Methods
Suicidal ideation and medical conditions were self-reported. Medical conditions were confirmed by medical records, medication use or clinical data where possible. MDD was determined using the Structured Clinical Interview for DSM-IV-TR Research Version, Non-patient edition.

Results
Of the 907 men, 8.5% reported suicidal ideation. Thyroid disorders (OR 3.85, 95%CI 1.2–12.1), syncope and seizures (OR 1.96, 95%CI 1.1–3.5), liver disorders (OR 3.53, 95%CI 1.1–11.8; younger men only) and alcoholism (OR 2.15, 95%CI 1.1–4.4) were associated with increased odds of suicidal ideation, independent of age and MDD. Major vascular events doubled the odds of suicidal ideation but this was explained by MDD. No association was evident with high medical burden, musculoskeletal disease, metabolic factors, gastrointestinal disorders, headaches, cardiovascular disease, COPD, cancer and psoriasis.

Conclusion
Health care professionals should focus on identification, assessment and management of suicidal ideation in the medically ill in patients both with and without MDD.

FracSym: automated symbolic computation of Lie symmetries of fractional differential equations

In this paper, we present an algorithm for the systematic calculation of Lie point symmetries for fractional order differential equations (FDEs) using the method as described by Buckwar & Luchko (1998) and Gazizov, Kasatkin & Lukashchuk (2007, 2009, 2011). The method has been generalised here to allow for the determination of symmetries for FDEs with n independent variables and for systems of partial FDEs. The algorithm has been implemented in the new MAPLE package FracSym (Jefferson and Carminati 2013) which uses routines from the MAPLE symmetry packages DESOLVII (Vu, Jefferson and Carminati, 2012) and ASP (Jefferson and Carminati, 2013). We introduce FracSym by investigating the symmetries of a number of FDEs; specific forms of any arbitrary functions, which may extend the symmetry algebras, are also determined. For each of the FDEs discussed, selected invariant solutions are then presented. © 2013 Elsevier B.V. All rights reserved.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.