Self-localization of a small number of Bose particles in a superfluid Fermi system

We consider self-localization of a small number of Bose particles immersed in a large homogeneous superfluid mixture of fermions in three and one dimensional spaces. Bosons distort the density of surrounding fermions and create a potential well where they can form a bound state analogous to a small polaron state. In the three dimensional volume we observe the self-localization for repulsive interactions between bosons and fermions. In the one dimensional case bosons self-localize as well as for attractive interactions forming, together with a pair of fermions at the bottom of the Fermi sea, a vector soliton. We analyze also thermal effects and show that small non-zero temperature affects the pairing function of the Fermi-subsystem and has little influence on the self-localization phenomena.Comment: 7 pages, 7 fiqures, improved versio

Automatic Environmental Sound Recognition: Performance versus Computational Cost

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost

Self-localized impurities embedded in a one dimensional Bose-Einstein condensate and their quantum fluctuations

We consider the self-localization of neutral impurity atoms in a Bose-Einstein condensate in a 1D model. Within the strong coupling approach, we show that the self-localized state exhibits parametric soliton behavior. The corresponding stationary states are analogous to the solitons of non-linear optics and to the solitonic solutions of the Schroedinger-Newton equation (which appears in models that consider the connection between quantum mechanics and gravitation). In addition, we present a Bogoliubov-de-Gennes formalism to describe the quantum fluctuations around the product state of the strong coupling description. Our fluctuation calculations yield the excitation spectrum and reveal considerable corrections to the strong coupling description. The knowledge of the spectrum allows a spectroscopic detection of the impurity self-localization phenomenon.Comment: 7 pages, 5 figure

Many-body Anderson localization in one dimensional systems

We show, using quasi-exact numerical simulations, that Anderson localization of one-dimensional particles in a disordered potential survives in the presence of attractive interaction between particles. The localization length of the composite particle can be computed analytically for weak disorder and is in good agreement with the quasi-exact numerical observations using Time Evolving Block Decimation. Our approach allows for simulation of the entire experiment including the final measurement of all atom positions.Comment: 12pp, 5 fig, version accepted in NJ

Implications of new sustainable greenhouse systems for pests, diseases and biological control : a modelling approach using Oidium neolycopersici and Tetranychus urticae

Concerns regarding carbon emissions, increasing demands on water supplies and environmental pollution have meant that the European protected horticulture industry is being challenged to develop more sustainable greenhouse climate management systems. These new systems can however potentially impact on pest and disease (P & D) pressures and the efficacy of biological control agents (BCAs). This thesis aimed to use a combination of experimental work and simulation models to compare novel and traditional greenhouse climate management scenarios in Spain and the Netherlands using two model P & D systems. These were Oidium neolycopersici (powdery mildew) and its BCA, Bacillus subtilis, on tomato, and Tetranychus urticae (the two-spotted spider mite) and its BCA, Phytoseiulus persimilis, on ornamentals. Experiments showed that latent period, disease development and sporulation of Oidium neolycopersici were strongly influenced by temperatures between 10-33°C and that the control efficacy of B. subtilis was significantly influenced by temperature and humidity in the ranges 10-33°C and 50-95% RH. The functional response of P. persimilis was found to be significantly affected by ambient humidities of 57-99% RH, with predation highest at 85% RH and lowest below 76% RH. These results, in combination with existing data, were used to construct dynamic P & D models. A greenhouse climate model, based on observed temperatures in European greenhouses, was constructed to provide data on the diurnal and seasonal variation in temperature and humidity for different climate management scenarios. The predictions from the P & D models allowed climate control regimes in different greenhouses in Spain and the Netherlands to be identified, which minimised P & D pressures and maximised the efficacy of the BCAs. The implications of these findings for greenhouse climate management are discussed

Insensitivity of flavoured leptogenesis to low energy CP violation

If the baryon asymmetry of the Universe is produced by leptogenesis, CP violation is required in the lepton sector. In the seesaw extension of the Standard Model with three hierarchical right-handed neutrinos, we show that the baryon asymmetry is insensitive to the PMNS phases: thermal leptogenesis can work for any value of the observable phases. This result was well-known when there are no flavour effects in leptogenesis; we show that it remains true when flavour effects are included.Comment: 4 pages, 1 figure; version accepted for publication, added explanations, notation clarifie

Visual comparative case analytics

Criminal Intelligence Analysis (CIA) faces a challenging task in handling high-dimensional data that needs to be investigated with complex analytical processes. State-of-the-art crime analysis tools do not fully support interactive data exploration and fall short of computational transparency in terms of revealing alternative results. In this paper we report our ongoing research into providing the analysts with such a transparent and interactive system for exploring similarities between crime cases. The system implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics (VA) workflow iteratively supports the interpretation of obtained clustering results, the development of alternative models, as well as cluster verification. The visualizations offer a usable way for the analyst to provide feedback to the system and to observe the impact of their interaction

Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

A fundamental task in Criminal Intelligence Analysis is to analyze the similarity of crime cases, called CCA, to identify common crime patterns and to reason about unsolved crimes. Typically, the data is complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users' trust in the results and hence a reluctance to use the tool}. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centred design decisions made this computational complexity less scary to criminal analysts

Solitons in coupled atomic-molecular Bose-Einstein condensates in a trap

We consider coupled atomic-molecular Bose-Einstein condensate system in a quasi-one-dimensional trap. In the vicinity of a Feshbach resonance the system can reveal soliton-like behavior. We analyze bright soliton solutions for the system in the trap and in the presence of the interactions between particles. We show that with increasing number of particles in the system two bright soliton solutions start resembling dark soliton profiles known in an atomic Bose-Einstein condensate with repulsive interactions between atoms. We analyze also methods for experimental preparation and detection of the soliton states.Comment: 7 pages, 7 figures, published versio