Fictions

Work in three movements for chamber ensemble of flute (d piccolo, alto flute), clarinet (d bass clarinet), violin, cello, piano written for Stony Brook Contemporary Music Players, Stony Brook University NY USA

Cidade Museu

Through the concept of sonic resonance, the project Cidade Museu – Museum City explores five derelict or transitional spaces in the city of Viseu. The activation and capture of these spaces develops an audio- visual memory that reflects architectures, stories and experiences, while creating a sense of place through sounds and images.

The project brings together musicians with a background in contemporary music, electroacoustic music and improvisation and a visual artist focusing on photography and video.

Each member of the collective explores the selected spaces in order to activate them with the help of their respective instruments and through sound projection in an iterative process in which the source of activation gradually gives way to the characteristics of each space, their resonances and acoustic characteristics. The museum city (a nickname for the city of Viseu), in this performance, exposes the contrast between the grandeur and multi-faceted architecture of Viseu’s Cathedral with spaces that spread throughout the city waiting for a new future.

The performance in the Cathedral (Sé) is characterised by a trio ensemble, an eight channel sound system and video projecting audio recordings and images made in each of the five spaces. The audience is invited to explore the relations between the various buildings and their stories while being immersed in their resonances and visual projections.

The performance explores the following spaces in Viseu: the old Orfeão (music hall), an old wine cellar, a mansion home to the national road services, a house with its grounds in Rua Silva Gaio and an old slaughterhouse.

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.

Cascaded multimodal biometric recognition framework

A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users' dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

Tools and Environments for Networked Electronic Performance

A collection of software and hardware tools and environments that facilitate collective networked performance between electronic musicians. Tools include 'Chat Monkey', a live chat tool for performance, 'DMA Sequencing', a step sequencer using open sound control messaging and multi nodal control, 'tutti, duet, trio, solo, quartet', an ensemble management environment, and 'Por Larrañaga', a cigar box based electro-acoustic instrument with embedded sensors and controllers. Notable performances: w/BLISS, NCAD, Dublin, 1 March 2015; w/BLISS, NI Science Festival, Belfast, 21 Feb 2015

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.