Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network

Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes

Isolating intrinsic noise sources in a stochastic genetic switch

The stochastic mutual repressor model is analysed using perturbation methods. This simple model of a gene circuit consists of two genes and three promotor states. Either of the two protein products can dimerize, forming a repressor molecule that binds to the promotor of the other gene. When the repressor is bound to a promotor, the corresponding gene is not transcribed and no protein is produced. Either one of the promotors can be repressed at any given time or both can be unrepressed, leaving three possible promotor states. This model is analysed in its bistable regime in which the deterministic limit exhibits two stable fixed points and an unstable saddle, and the case of small noise is considered. On small time scales, the stochastic process fluctuates near one of the stable fixed points, and on large time scales, a metastable transition can occur, where fluctuations drive the system past the unstable saddle to the other stable fixed point. To explore how different intrinsic noise sources affect these transitions, fluctuations in protein production and degradation are eliminated, leaving fluctuations in the promotor state as the only source of noise in the system. Perturbation methods are then used to compute the stability landscape and the distribution of transition times, or first exit time density. To understand how protein noise affects the system, small magnitude fluctuations are added back into the process, and the stability landscape is compared to that of the process without protein noise. It is found that significant differences in the random process emerge in the presence of protein noise

Families and Communities Against Child Sexual Exploitation (FCASE) : final evaluation report

This is the final evaluation report for the Barnardo’s Families and Communities Against Sexual Exploitation project (FCASE), produced by the International Centre, researching Child Sexual Exploitation, Violence and Trafficking at the University of Bedfordshire. The programme was launched in April 2013, funded by the Department for Education (DfE) and concluded in March 2015. The evaluation was undertaken during the same period. The FCASE model has been piloted in three sites, which for the purposes of this report have been anonymised and will be referred to using pseudonyms. It consists of the following elements: a structured programme of six to eight weeks direct work with young people and families where a risk of child sexual exploitation (CSE) has been identified; delivery of CSE training with professionals; and undertaking community awareness raising. The evaluation has been informed by a range of qualitative data. The report identifies the elements that work well and some of the challenges in its implementation. This had been done in order to determine good practice in supporting families and communities and embed more effective practice on protecting children and young people, including those in foster care, from sexual exploitation, harnessing the protective factors within a child’s family and/or foster home. The learning from the project is intended to help other agencies to implement the FCASE model. An on-line learning resource is to be produced in order to facilitate this process

Influence of the Ground-State Topology on the Domain-Wall Energy in the Edwards-Anderson +/- J Spin Glass Model

We study the phase stability of the Edwards-Anderson spin-glass model by analyzing the domain-wall energy. For the bimodal distribution of bonds, a topological analysis of the ground state allows us to separate the system into two regions: the backbone and its environment. We find that the distributions of domain-wall energies are very different in these two regions for the three dimensional (3D) case. Although the backbone turns out to have a very high phase stability, the combined effect of these excitations and correlations produces the low global stability displayed by the system as a whole. On the other hand, in two dimensions (2D) we find that the surface of the excitations avoids the backbone. Our results confirm that a narrow connection exists between the phase stability of the system and the internal structure of the ground-state. In addition, for both 3D and 2D we are able to obtain the fractal dimension of the domain wall by direct means.Comment: 4 pages, 3 figures. Accepted for publication in Rapid Communications of Phys. Rev.

Reducing the Learning Domain by Using Image Processing to Diagnose COVID-19 from X-Ray Image

Over the last months, dozens of artificial intelligence (AI) solutions for COVID-19 diagnosis based on chest X-ray image analysis have been proposed. All of them with very impressive sensitivity and specificity results. However, its generalization and translation to the clinical practice are rather challenging due to the discrepancies between domain distributions when training and test data come from different sources. Consequently, applying a trained model on a new data set may have a problem with domain adaptation leading to performance degradation. This research aims to study the impact of image pre-processing on pre-trained deep learning models to reduce the learning domain. The dataset used in this research consists of 5,000 X-ray images obtained from different sources under two categories: negative and positive COVID-19 detection. We implemented transfer learning in 3 popular convolutional neural networks (CNNs), including VGG16, VGG19, and DenseNet169. We repeated the study following the same structure for original and pre-processed images. The pre-processing method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter application and image registration. After evaluating the models, the CNNs that have been trained with pre-processed images obtained an accuracy score up to 1.2% better than the unprocessed ones. Furthermore, we can observe that in the 3 CNN models, the repeated misclassified images represent 40.9% (207/506) of the original image dataset with the erroneous result. In pre-processed ones, this percentage is 48.9% (249/509). In conclusion, image processing techniques can help to reduce the learning domain for deep learning applications

Risk management as a basis for integrated water cycle management in Kazakhstan

Integrated Water Cycle Management (IWCM) aims to bring together a diversity of social, environmental, technological and economic aspects to implement sustainable water and land management systems. This paper investigates the challenges and opportunities facing Kazakhstan as it its efforts to move towards a more sustainable approach to managing its finite and highly stressed water resources. The use of a strategic-level risk governance framework to support a multi-disciplinary Kazakh-EU consortium in working collabora-tively towards enhancing capacity and capability to address identified challenges is described. With a clear focus on addressing capacity building needs, a strong emphasis is placed on developing taught integrated water cycle management programmes through communi-cation, stakeholder engagement and policy development including appropriate tools for managing the water issues including hydraulic models, GIS-based systems and scenario developments. Conclusions on the benefits of implementing an EU-style Water Framework Directive for Central Asia based on a risk management approach in Kazakhstan are formulated