Seeking the New Normal? Troubled Spaces of Encountering Visible Differences in Warsaw

In times of globalisation and super-mobility, ideas of normality are in turmoil. In different societies in, across and beyond Europe, we face the challenge of undoing specific notions of normality and creating more inclusive societies with an open culture of learning to live with differences. The scope of
the paper is to introduce some findings on encounters with difference and negotiations of social values in relation to a growing visibility of difference after 1989 in Poland, on the background of a critique of normality/normalisation and normalcy.On the basis of interviews conducted inWarsaw, we investigate how normality/normalisation discourses of visible homosexuality and physical disability are incorporated into individual self-reflections and justifications of prejudices (homophobia and disabilism). More specifically we argue that there are moments of ‘cultural transgressions’ present in everyday practices towards ‘visible’sexual and (dis)ability difference.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

Violation of Bell inequalities in larger Hilbert spaces: robustness and challenges

We explore the challenges posed by the violation of Bell-like inequalities by d-dimensional systems exposed to imperfect state-preparation and measurement settings. We address, in particular, the limit of high-dimensional systems, naturally arising when exploring the quantum-to-classical transition. We show that, although suitable Bell inequalities can be violated, in principle, for any dimension of given subsystems, it is in practice increasingly challenging to detect such violations, even if the system is prepared in a maximally entangled state. We characterize the effects of random perturbations on the state or on the measurement settings, also quantifying the efforts needed to certify the possible violations in case of complete ignorance on the system state at hand.

Wounded Spaces: When Planning Degraded Cairo’s Urban Memory

This paper examines the position of planning practices operated under precise guidelines for displaying modernity. Cultivating the spatial qualities of Cairo since the 1970s has unveiled centralised ideologies and systems of governance and economic incentives. I present a discussion of the wounds that result from the inadequate upgrading ventures in Cairo, which I argue, created scars as enduring evidence of unattainable planning methods and processes that undermined its locales. In this process, the paper focuses on the consequences of eviction rather than the planning methods in one of the city’s traditional districts. Empirical work is based on interdisciplinary research, public media reports and archival maps that document actions and procedures put in place to alter the visual, urban, and demographic characteristics of Cairo’s older neighbourhoods against a backdrop of decay to shift towards a global spectacular. The paper builds a conversation about the power and fate these spaces were subject to during hostile transformations that ended with their being disused. Their existence became associated with sores on the souls of its ex-inhabitants, as outward signs of inward scars showcasing a lack of equality and social justice in a context where it was much needed.

Investigation into DCT Feature Selection for Visual Lip-Based Biometric Authentication

This paper investigated using lip movements as a behavioural biometric for person authentication. The system was trained, evaluated and tested using the XM2VTS dataset, following the Lausanne Protocol configuration II. Features were selected from the DCT coefficients of the greyscale lip image. This paper investigated the number of DCT coefficients selected, the selection process, and static and dynamic feature combinations. Using a Gaussian Mixture Model - Universal Background Model framework an Equal Error Rate of 2.20% was achieved during evaluation and on an unseen test set a False Acceptance Rate of 1.7% and False Rejection Rate of 3.0% was achieved. This compares favourably with face authentication results on the same dataset whilst not being susceptible to spoofing attacks.

Precarious spaces and urban change in post-conflict Belfast

The Northern Ireland conflict is shaped by an ethno-national contest between a minority Catholic/Nationalist/Republican population who broadly want to see the reunification of Ireland; and a majority Protestant/Unionist/Loyalist one, who mainly wish to maintain the sovereign connection with Britain. After nearly three decades of violence, which intensified segregation in schooling, labour markets and especially housing, a Peace Agreement was signed on Good Friday 1998. This paper is concerned with the peace process after the Agreement, not so much for the ambiguous political compromise, but for the way in which the city is constitutive of transformation and how Belfast in particular, is now embedded with a range of social instabilities and spatial contradictions. The Agreement encouraged rapid economic expansion, inward investment, especially in knowledge–intensive sectors and a short-lived optimism that markets and the neo-liberal fix would drive the post-conflict, post-industrial and post-political city. Capital would trump ethnicity and the economic uplift would bind citizens to a new expression of hope based on property speculation, tourism and global corporate investment.

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy