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Watching movies and TV shows with subtitles enabled is not simply down to audibility or speech intelligibility. A variety of evolving factors related to technological advances, cinema production and social behaviour challenge our perception and understanding. This study seeks to formalise and give context to these influential factors under a wider and novel term referred to as Dialogue Understandability. We propose a working definition for Dialogue Understandability being a listener's capacity to follow the story without undue cognitive effort or concentration being required that impacts their Quality of Experience (QoE). The paper identifies, describes and categorises the factors that influence Dialogue Understandability mapping them over the QoE framework, a media streaming lifecycle, and the stakeholders involved. We then explore available measurement tools in the literature and link them to the factors they could potentially be used for. The maturity and suitability of these tools is evaluated over a set of pilot experiments. Finally, we reflect on the gaps that still need to be filled, what we can measure and what not, future subjective experiments, and new research trends that could help us to fully characterise Dialogue Understandability.

This paper presents NOMAD (Non-Matching Audio Distance), a differentiable perceptual similarity metric that measures the distance of a degraded signal against non-matching references. The proposed method is based on learning deep feature embeddings via a triplet loss guided by the Neurogram Similarity Index Measure (NSIM) to capture degradation intensity. During inference, the similarity score between any two audio samples is computed through Euclidean distance of their embeddings. NOMAD is fully unsupervised and can be used in general perceptual audio tasks for audio analysis e.g. quality assessment and generative tasks such as speech enhancement and speech synthesis. The proposed method is evaluated with 3 tasks. Ranking degradation intensity, predicting speech quality, and as a loss function for speech enhancement. Results indicate NOMAD outperforms other non-matching reference approaches in both ranking degradation intensity and quality assessment, exhibiting competitive performance with full-reference audio metrics. NOMAD demonstrates a promising technique that mimics human capabilities in assessing audio quality with non-matching references to learn perceptual embeddings without the need for human-generated labels., Comment: Accepted for ICASSP 2024

Self-supervised representation learning (SSRL) has demonstrated superior performance than supervised models for tasks including phoneme recognition. Training SSRL models poses a challenge for low-resource languages where sufficient pre-training data may not be available. A common approach is cross-lingual pre-training. Instead, we propose to use audio augmentation techniques, namely: pitch variation, noise addition, accented target language and other language speech to pre-train SSRL models in a low resource condition and evaluate phoneme recognition. Our comparisons found that a combined synthetic augmentations (noise/pitch) strategy outperformed accent and language knowledge transfer. Furthermore, we examined the scaling factor of augmented data to achieve equivalent performance to model pre-trained with target domain speech. Our findings suggest that for resource-constrained languages, combined augmentations can be a viable option than other augmentations., Comment: Paper accepted in Interspeech2024

Intentional and accidental harms arising from the use of AI have impacted the health, safety and rights of individuals. While regulatory frameworks are being developed, there remains a lack of consensus on methods necessary to deliver safe AI. The potential for explainable AI (XAI) to contribute to the effectiveness of the regulation of AI is being increasingly examined. Regulation must include methods to ensure compliance on an ongoing basis, though there is an absence of practical proposals on how to achieve this. For XAI to be successfully incorporated into a regulatory system, the individuals who are engaged in interpreting/explaining the model to stakeholders should be sufficiently qualified for the role. Statutory professionals are prevalent in domains in which harm can be done to the health, safety and rights of individuals. The most obvious examples are doctors, engineers and lawyers. Those professionals are required to exercise skill and judgement and to defend their decision making process in the event of harm occurring. We propose that a statutory profession framework be introduced as a necessary part of the AI regulatory framework for compliance and monitoring purposes. We will refer to this new statutory professional as an AI Architect (AIA). This AIA would be responsible to ensure the risk of harm is minimised and accountable in the event that harms occur. The AIA would also be relied on to provide appropriate interpretations/explanations of XAI models to stakeholders. Further, in order to satisfy themselves that the models have been developed in a satisfactory manner, the AIA would require models to have appropriate transparency. Therefore it is likely that the introduction of an AIA system would lead to an increase in the use of XAI to enable AIA to discharge their professional obligations., Comment: Accepted for publication at xAI-2023 conference

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a large variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches - which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals., Comment: Submitted to Computers in Biology and Medicine Journal

Learning music representations that are general-purpose offers the flexibility to finetune several downstream tasks using smaller datasets. The wav2vec 2.0 speech representation model showed promising results in many downstream speech tasks, but has been less effective when adapted to music. In this paper, we evaluate whether pre-training wav2vec 2.0 directly on music data can be a better solution instead of finetuning the speech model. We illustrate that when pre-training on music data, the discrete latent representations are able to encode the semantic meaning of musical concepts such as pitch and instrument. Our results show that finetuning wav2vec 2.0 pre-trained on music data allows us to achieve promising results on music classification tasks that are competitive with prior work on audio representations. In addition, the results are superior to the pre-trained model on speech embeddings, demonstrating that wav2vec 2.0 pre-trained on music data can be a promising music representation model., Comment: Submitted to ICASSP 2023

Non-reference speech quality models are important for a growing number of applications. The VoiceMOS 2022 challenge provided a dataset of synthetic voice conversion and text-to-speech samples with subjective labels. This study looks at the amount of variance that can be explained in subjective ratings of speech quality from metadata and the distribution imbalances of the dataset. Speech quality models were constructed using wav2vec 2.0 with additional metadata features that included rater groups and system identifiers and obtained competitive metrics including a Spearman rank correlation coefficient (SRCC) of 0.934 and MSE of 0.088 at the system-level, and 0.877 and 0.198 at the utterance-level. Using data and metadata that the test restricted or blinded further improved the metrics. A metadata analysis showed that the system-level metrics do not represent the model's system-level prediction as a result of the wide variation in the number of utterances used for each system on the validation and test datasets. We conclude that, in general, conditions should have enough utterances in the test set to bound the sample mean error, and be relatively balanced in utterance count between systems, otherwise the utterance-level metrics may be more reliable and interpretable., Comment: Preprint; accepted for Interspeech 2022

Speech synthesis quality prediction has made remarkable progress with the development of supervised and self-supervised learning (SSL) MOS predictors but some aspects related to the data are still unclear and require further study. In this paper, we evaluate several MOS predictors based on wav2vec 2.0 and the NISQA speech quality prediction model to explore the role of the training data, the influence of the system type, and the role of cross-domain features in SSL models. Our evaluation is based on the VoiceMOS challenge dataset. Results show that SSL-based models show the highest correlation and lowest mean squared error compared to supervised models. The key point of this study is that benchmarking the statistical performance of MOS predictors alone is not sufficient to rank models since potential issues hidden in the data could bias the evaluated performances., Comment: Accepted ISSC 2023

Recent studies have shown how self-supervised models can produce accurate speech quality predictions. Speech representations generated by the pre-trained wav2vec 2.0 model allows constructing robust predicting models using small amounts of annotated data. This opens the possibility of developing strong models in scenarios where labelled data is scarce. It is known that fine-tuning improves the model's performance; however, it is unclear how the data (e.g., language, amount of samples) used for fine-tuning is influencing that performance. In this paper, we explore how using different speech corpus to fine-tune the wav2vec 2.0 can influence its performance. We took four speech datasets containing degradations found in common conferencing applications and fine-tuned wav2vec 2.0 targeting different languages and data size scenarios. The fine-tuned models were tested across all four conferencing datasets plus an additional dataset containing synthetic speech and they were compared against three external baseline models. Results showed that fine-tuned models were able to compete with baseline models. Larger fine-tune data guarantee better performance; meanwhile, diversity in language helped the models deal with specific languages. Further research is needed to evaluate other wav2vec 2.0 models pre-trained with multi-lingual datasets and to develop prediction models that are more resilient to language diversity., Comment: Submitted to Interspeech 2022

The Quality of Experience (QoE) based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction and resource management where machine learning (ML) can play a crucial role. In this paper, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. Firstly, we provide a detailed pipeline for developing and deploying supervised learning-based video streaming QoE prediction models which covers several stages including data collection, feature engineering, model optimization and training, testing and prediction and evaluation. Secondly, we discuss the deployment of the ML model for the QoE prediction/measurement in the next generation networks (5G/6G) using network enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) by proposing reference architecture. Thirdly, we present a comparative study of the state-of-the-art supervised learning ML models for QoE prediction of video streaming applications based on multiple performance metrics.

Audio quality assessment has been widely researched in the signal processing area. Full-reference objective metrics (e.g., POLQA, ViSQOL) have been developed to estimate the audio quality relying only on human rating experiments. To evaluate the audio quality of novel audio processing techniques, researchers constantly need to compare objective quality metrics. Testing different implementations of the same metric and evaluating new datasets are fundamental and ongoing iterative activities. In this paper, we present AQP - an open-source, node-based, light-weight Python pipeline for audio quality assessment. AQP allows researchers to test and compare objective quality metrics helping to improve robustness, reproducibility and development speed. We introduce the platform, explain the motivations, and illustrate with examples how, using AQP, objective quality metrics can be (i) compared and benchmarked; (ii) prototyped and adapted in a modular fashion; (iii) visualised and checked for errors. The code has been shared on GitHub to encourage adoption and contributions from the community., Comment: 6 pages, 3 figures, accepted and presented at ACM MMSys22, June, 2022, Athlone, Ireland

Non-intrusive speech quality assessment is a crucial operation in multimedia applications. The scarcity of annotated data and the lack of a reference signal represent some of the main challenges for designing efficient quality assessment metrics. In this paper, we propose two multi-task models to tackle the problems above. In the first model, we first learn a feature representation with a degradation classifier on a large dataset. Then we perform MOS prediction and degradation classification simultaneously on a small dataset annotated with MOS. In the second approach, the initial stage consists of learning features with a deep clustering-based unsupervised feature representation on the large dataset. Next, we perform MOS prediction and cluster label classification simultaneously on a small dataset. The results show that the deep clustering-based model outperforms the degradation classifier-based model and the 3 baselines (autoencoder features, P.563, and SRMRnorm) on TCD-VoIP. This paper indicates that multi-task learning combined with feature representations from unlabelled data is a promising approach to deal with the lack of large MOS annotated datasets., Comment: Published in 2021 13th International Conference on Quality of Multimedia Experience (QoMEX)

Good speech quality has been achieved using waveform matching and parametric reconstruction coders. Recently developed very low bit rate generative codecs can reconstruct high quality wideband speech with bit streams less than 3 kb/s. These codecs use a DNN with parametric input to synthesise high quality speech outputs. Existing objective speech quality models (e.g., POLQA, ViSQOL) do not accurately predict the quality of coded speech from these generative models underestimating quality due to signal differences not highlighted in subjective listening tests. We present WARP-Q, a full-reference objective speech quality metric that uses dynamic time warping cost for MFCC speech representations. It is robust to small perceptual signal changes. Evaluation using waveform matching, parametric and generative neural vocoder based codecs as well as channel and environmental noise shows that WARP-Q has better correlation and codec quality ranking for novel codecs compared to traditional metrics in addition to versatility for general quality assessment scenarios., Comment: Accepted for presentation at IEEE ICASSP 2021. Source code and data can be found on https://github.com/wjassim/WARP-Q.git

Is a new regulated profession, such as Artificial Intelligence (AI) Architect who is responsible and accountable for AI outputs necessary to ensure trustworthy AI? AI is becoming all pervasive and is often deployed in everyday technologies, devices and services without our knowledge. There is heightened awareness of AI in recent years which has brought with it fear. This fear is compounded by the inability to point to a trustworthy source of AI, however even the term "trustworthy AI" itself is troublesome. Some consider trustworthy AI to be that which complies with relevant laws, while others point to the requirement to comply with ethics and standards (whether in addition to or in isolation of the law). This immediately raises questions of whose ethics and which standards should be applied and whether these are sufficient to produce trustworthy AI in any event., Comment: To be published in The Second Workshop on Implementing Machine Ethics, Dublin, Ireland, 30 June 2020

With the coming of age of virtual/augmented reality and interactive media, numerous definitions, frameworks, and models of immersion have emerged across different fields ranging from computer graphics to literary works. Immersion is oftentimes used interchangeably with presence as both concepts are closely related. However, there are noticeable interdisciplinary differences regarding definitions, scope, and constituents that are required to be addressed so that a coherent understanding of the concepts can be achieved. Such consensus is vital for paving the directionality of the future of immersive media experiences (IMEx) and all related matters. The aim of this white paper is to provide a survey of definitions of immersion and presence which leads to a definition of immersive media experience (IMEx). The Quality of Experience (QoE) for immersive media is described by establishing a relationship between the concepts of QoE and IMEx followed by application areas of immersive media experience. Influencing factors on immersive media experience are elaborated as well as the assessment of immersive media experience. Finally, standardization activities related to IMEx are highlighted and the white paper is concluded with an outlook related to future developments.

Estimation of perceptual quality in audio and speech is possible using a variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for speech and audio, respectively,) provides improvements upon previous versions, in terms of both design and usage. As an open source C++ library or binary with permissive licensing, ViSQOL can now be deployed beyond the research context into production usage. The feedback from internal production teams at Google has helped to improve this new release, and serves to show cases where it is most applicable, as well as to highlight limitations. The new model is benchmarked against real-world data for evaluation purposes. The trends and direction of future work is discussed., Comment: 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

Quality of Experience (QoE) for various types of websites has gained significant attention in recent years. In order to design and evaluate websites, a metric that can estimate a user's experienced quality robustly for diverse content is necessary. SpeedIndex (SI) has been widely adopted to estimate perceived web page loading progress. It measures the speed of rendering pixels for the webpage that is visible in the browser window. This is termed Above-The-Fold (ATF). The influence of animated content on the perception of ATF has been less comprehensively explored. In this paper, we present an experimental design and methodology to measure ATF perception for websites with and without animated elements for various page content categories. We found that pages with animated elements caused people to have more varied perceptions of ATF under different network conditions. Animated content also impacts the page load estimation accuracy of SI for websites. We discuss how the difference in the perception of ATF will impact the QoE management of web applications. We explain the necessity of revisiting the visual assessment of ATF to include the animated contents and improve the robustness of metrics like SI.

This study compares the performances of different algorithms for coding speech at low bit rates. In addition to widely deployed traditional vocoders, a selection of recently developed generative-model-based coders at different bit rates are contrasted. Performance analysis of the coded speech is evaluated for different quality aspects: accuracy of pitch periods estimation, the word error rates for automatic speech recognition, and the influence of speaker gender and coding delays. A number of performance metrics of speech samples taken from a publicly available database were compared with subjective scores. Results from subjective quality assessment do not correlate well with existing full reference speech quality metrics. The results provide valuable insights into aspects of the speech signal that will be used to develop a novel metric to accurately predict speech quality from generative-model-based coders., Comment: 6 pages, 11 figures, conference

The development of audio-visual quality assessment models poses a number of challenges in order to obtain accurate predictions. One of these challenges is the modelling of the complex interaction that audio and visual stimuli have and how this interaction is interpreted by human users. The No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd) deals with this problem from a machine learning perspective. The metric receives two sets of audio and video features descriptors and produces a low-dimensional set of features used to predict the audio-visual quality. A basic implementation of NAViDAd was able to produce accurate predictions tested with a range of different audio-visual databases. The current work performs an ablation study on the base architecture of the metric. Several modules are removed or re-trained using different configurations to have a better understanding of the metric functionality. The results presented in this study provided important feedback that allows us to understand the real capacity of the metric's architecture and eventually develop a much better audio-visual quality metric.

Telepresence robots (TPRs) are versatile, remotely controlled vehicles that enable physical presence and human-to-human interaction over a distance. Thanks to improving hardware and dropping price points, TPRs enjoy the growing interest in various industries and application domains. Still, a satisfying experience remains key for their acceptance and successful adoption, not only in terms of enabling remote communication with others, but also in terms of managing robot mobility by means of remote navigation. This paper focuses on the latter aspect of remote operation which has been hitherto neglected. We present the results of an extensive subjective study designed to systematically assess remote navigation Quality of Experience (QoE) in the context of using a TPR live over the Internet. Participants were 'beamed' into a remote office space and asked to perform characteristic TPR remote operation tasks (driving, turning, parking). Visual and control dimensions of their experience were systematically impaired by altering network characteristics (bandwidth, delay and packet loss rate) in a controlled fashion. Our results show that users can differentiate well between visual and navigation/control aspects of their experience. Furthermore, QoE impairment sensitivity varies with the actual task at hand.

Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, hand-crafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy., Comment: This publication has been accepted in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks., Comment: 40 Pages, 22 figures, published in computer networks (Open Access)

The n-dimensional hypercube quantum random walk (QRW) is a particularily appealing example of a quantum walk because it has a natural implementation on a register on $n$ qubits. However, any real implementation will encounter decoherence effects due to interactions with uncontrollable degrees of freedom. We present a complete characterization of the mixing properties of the hypercube QRW under a physically relevant Markovian decoherence model. In the local decoherence model considered the non-unitary dynamics are modeled as a sum of projections on individual qubits to an arbitrary direction on the Bloch sphere. We prove that there is always classical (asymptotic) mixing in this model and specify the conditions under which instantaneous mixing \textit{always} exists. And we show that the latter mixing property, as well as the classical mixing time, depend heavily on the exact environmental interaction and its strength. Therefore, algorithmic applications of the QRW on the hypercube, if they intend to employ mixing properties, need to consider both the walk dynamics and the precise decoherence model., Comment: 18 pages, 3 figures. Published version. Spelling of author name changed, no content changes in v4

We derive and analyze the Born-Markov master equation for a quantum harmonic oscillator interacting with a bath of independent two-level systems. This hitherto virtually unexplored model plays a fundamental role as one of the four "canonical" system-environment models for decoherence and dissipation. To investigate the influence of further couplings of the environmental spins to a dissipative bath, we also derive the master equation for a harmonic oscillator interacting with a single spin coupled to a bosonic bath. Our models are experimentally motivated by quantum-electromechanical systems and micron-scale ion traps. Decoherence and dissipation rates are found to exhibit temperature dependencies significantly different from those in quantum Brownian motion. In particular, the systematic dissipation rate for the central oscillator decreases with increasing temperature and goes to zero at zero temperature, but there also exists a temperature-independent momentum-diffusion (heating) rate., Comment: 8 pages, 3 figures

Decoherence is the major stumbling block in the realization of a large-scale quantum computer. Ingenious methods have been devised to overcome decoherence, but their success has been proven only for over-simplified models of system-environment interaction. Whether such methods will be reliable in the face of more realistic models is a fundamental open question. In this partly pedagogical article, we study two toy models of quantum information processing, using the language of \emph{quantum walks}. Decoherence is incorporated in 3 ways - by coupling to a noisy `projective measurement' system, and by coupling to oscillator and spin baths., Comment: 8 pages, 3 figures; to appear in Theory Canada 3 special issue if the Canadian Journal of Physics

The physics of quantum walks on graphs is formulated in Hamiltonian language, both for simple quantum walks and for composite walks, where extra discrete degrees of freedom live at each node of the graph. It is shown how to map between quantum walk Hamiltonians and Hamiltonians for qubit systems and quantum circuits; this is done for both a single- and multi-excitation coding, and for more general mappings. Specific examples of spin chains, as well as static and dynamic systems of qubits, are mapped to quantum walks, and walks on hyperlattices and hypercubes are mapped to various gate systems. We also show how to map a quantum circuit performing the quantum Fourier transform, the key element of Shor's algorithm, to a quantum walk system doing the same. The results herein are an essential preliminary to a Hamiltonian formulation of quantum walks in which coupling to a dynamic quantum environment is included., Comment: 17 pages, 10 figures

What is the computational power of a quantum computer? We show that determining the output of a quantum computation is equivalent to counting the number of solutions to an easily computed set of polynomials defined over the finite field Z_2. This connection allows simple proofs to be given for two known relationships between quantum and classical complexity classes., Comment: 7 pages, 3 figures

The monogamous nature of entanglement has been illustrated by the derivation of entanglement sharing inequalities - bounds on the amount of entanglement that can be shared amongst the various parts of a multipartite system. Motivated by recent studies of decoherence, we demonstrate an interesting manifestation of this phenomena that arises in system-environment models where there exists interactions between the modes or subsystems of the environment. We investigate this phenomena in the spin-bath environment, constructing an entanglement sharing inequality bounding the entanglement between a central spin and the environment in terms of the pairwise entanglement between individual bath spins. The relation of this result to decoherence will be illustrated using simplified system-bath models of decoherence., Comment: 5 pages, 1 figure v2: 6 pages 2 figures, additional example and references

We compare and contrast the entanglement in the ground state of two Jahn-Teller models. The $E\otimes\beta$ system models the coupling of a two-level electronic system, or qubit, to a single oscillator mode, while the $E\otimes\epsilon$ models the qubit coupled to two independent, degenerate oscillator modes. In the absence of a transverse magnetic field applied to the qubit, both systems exhibit a degenerate ground state. Whereas there always exists a completely separable ground state in the $E\otimes\beta$ system, the ground states of the $E\otimes\epsilon$ model always exhibit entanglement. For the $E\otimes\beta$ case we aim to clarify results from previous work, alluding to a link between the ground state entanglement characteristics and a bifurcation of a fixed point in the classical analogue. In the $E\otimes\epsilon$ case we make use of an ansatz for the ground state. We compare this ansatz to exact numerical calculations and use it to investigate how the entanglement is shared between the three system degrees of freedom., Comment: 11 pages, 9 figures, comments welcome; 2 references added

How does the classical phase space structure for a composite system relate to the entanglement characteristics of the corresponding quantum system? We demonstrate how the entanglement in nonlinear bipartite systems can be associated with a fixed point bifurcation in the classical dynamics. Using the example of coupled giant spins we show that when a fixed point undergoes a supercritical pitchfork bifurcation, the corresponding quantum state - the ground state - achieves its maximum amount of entanglement near the critical point. We conjecture that this will be a generic feature of systems whose classical limit exhibits such a bifurcation., Comment: v2: Structure of the paper changed for clarity, reduced length, now 9 pages with 6 figures

We investigate the entaglement characteristics of two general bimodal Bose-Einstein condensates - a pair of tunnel-coupled Bose-Einstein condensates and the atom-molecule Bose-Einstein condensate. We argue that the entanglement is only physically meaningful if the system is viewed as a bipartite system, where the subsystems are the two modes. The indistinguishibility of the particles in the condensate means that the atomic constituents are physically inaccessible and thus the degree of entanglement between individual particles, unlike the entanglement between the modes, is not experimentally relevant so long as the particles remain in the condensed state. We calculate the entanglement between the modes for the exact ground state of the two bimodal condensates and consider the dynamics of the entanglement in the tunnel-coupled case., Comment: 11 pages, 8 figures, submitted to Physical Review A, to be presented at the third UQ Mathematical Physics workshop, Oct. 4-6; changes made in response to referee comments

How useful is a quantum dynamical operation for quantum information processing? Motivated by this question we investigate several strength measures quantifying the resources intrinsic to a quantum operation. We develop a general theory of such strength measures, based on axiomatic considerations independent of state-based resources. The power of this theory is demonstrated with applications to quantum communication complexity, quantum computational complexity, and entanglement generation by unitary operations., Comment: 19 pages, shortened by 3 pages, mainly cosmetic changes