A Magnetic Resonance Realization of Decoherence-Free Quantum Computation

We report the realization, using nuclear magnetic resonance techniques, of the first quantum computer that reliably executes an algorithm in the presence of strong decoherence. The computer is based on a quantum error avoidance code that protects against a class of multiple-qubit errors. The code stores two decoherence-free logical qubits in four noisy physical qubits. The computer successfully executes Grover's search algorithm in the presence of arbitrarily strong engineered decoherence. A control computer with no decoherence protection consistently fails under the same conditions.Comment: 5 pages with 3 figures, revtex4, accepted by Physical Review Letters; v2 minor revisions to conten

Where form and substance meet: using the narrative approach of re-storying to generate research findings and community rapprochement in (university) mathematics education

Storytelling is an engaging way through which lived experience can be shared and reflected upon, and a tool through which difference, diversity—and even conflict—can be acknowledged and elaborated upon. Narrative approaches to research bring the richness and vibrancy of storytelling into how data is collected and interpretations of it shared. In this paper, I demonstrate the potency of the narrative approach of re-storying for a certain type of university mathematics education research (non-deficit, non-prescriptive, context-specific, example-centred and mathematically focused) conducted at the interface of two communities: mathematics education and mathematics. I do so through reference to Amongst Mathematicians (Nardi, 2008), a study carried out in collaboration with 20 university mathematicians from six UK mathematics departments. The study deployed re-storying to present data and analyses in the form of a dialogue between two fictional, yet entirely data-grounded, characters—M, mathematician, and RME, researcher in mathematics education. In the dialogues, the typically conflicting epistemologies—and mutual perceptions of such epistemologies—of the two communities come to the fore as do the feasibility-of, benefits-from, obstacles-in and conditions-for collaboration between these communities. First, I outline the use of narrative approaches in mathematics education research. Then, I introduce the study and its use of re-storying, illustrating this with an example: the construction of a dialogue from interview data in which the participating mathematicians discuss the potentialities and pitfalls of visualisation in university mathematics teaching. I conclude by outlining re-storying as a vehicle for community rapprochement achieved through generating and sharing research findings—the substance of research—in forms that reflect the fundamental principles and aims that underpin this research. My conclusions resonate with sociocultural constructs that view mathematics teacher education as contemporary praxis and the aforementioned inter-community discussion as taking place within a third space

Insights on neural representations for end-to-end speech recognition

End-to-end automatic speech recognition (ASR) models aim to learn a generalised speech representation. However, there are limited tools available to understand the internal functions and the effect of hierarchical dependencies within the model architecture. It is crucial to understand the correlations between the layer-wise representations, to derive insights on the relationship between neural representations and performance. Previous investigations of network similarities using correlation analysis techniques have not been explored for End-to-End ASR models. This paper analyses and explores the internal dynamics between layers during training with CNN, LSTM and Transformer based approaches using Canonical correlation analysis (CCA) and centered kernel alignment (CKA) for the experiments. It was found that neural representations within CNN layers exhibit hierarchical correlation dependencies as layer depth increases but this is mostly limited to cases where neural representation correlates more closely. This behaviour is not observed in LSTM architecture, however there is a bottom-up pattern observed across the training process, while Transformer encoder layers exhibit irregular coefficiency correlation as neural depth increases. Altogether, these results provide new insights into the role that neural architectures have upon speech recognition performance. More specifically, these techniques can be used as indicators to build better performing speech recognition models

Two-dimensional NMR lineshape analysis

NMR titration experiments are a rich source of structural, mechanistic, thermodynamic and kinetic information on biomolecular interactions, which can be extracted through the quantitative analysis of resonance lineshapes. However, applications of such analyses are frequently limited by peak overlap inherent to complex biomolecular systems. Moreover, systematic errors may arise due to the analysis of two-dimensional data using theoretical frameworks developed for one-dimensional experiments. Here we introduce a more accurate and convenient method for the analysis of such data, based on the direct quantum mechanical simulation and fitting of entire two-dimensional experiments, which we implement in a new software tool, TITAN (TITration ANalysis). We expect the approach, which we demonstrate for a variety of protein-protein and protein-ligand interactions, to be particularly useful in providing information on multi-step or multi-component interactions

Insights of neural representations in multi-banded and multi-channel convolutional transformers for end-to-end ASR

End-to-End automatic speech recognition (ASR) models aim to learn generalised representations of speech. Popular approaches for End-to-End solutions have involved utilising extremely large amounts of data and large models to im-prove recognition performance. However, it is not clear if these models are generalising the training data or memorising the data. This paper combines the power of a mixture of experts (MoE) models, which is referred to as multi-band, multi-channel, with a popular model for ASR, the CNN-transformer, to capture longer-term dependencies without increasing the computational complexity of training. The goal is to investigate how the transformer models adapt to these different input representations of the same data. No external language models were used to remove the impact of external language models during inference. Although the proposed multi-band transformer shows performance gain, the main finding of this paper is to show the adaptive memo-risation nature of transformers and the neural representations of transformer embedding. Using the statistical correlation index SVCCA, comparative discussion of the neural repre-sentations of the proposed model and transformer approach is provided, with key insights into the distinct learned structures

The application of digital tools for knowledge sharing in agriculture : a longitudinal case study from four Australian grower groups

Context: Digital tools and platforms are universally used, however their application for knowledge and information sharing in agriculture is less understood. In Australia, grower groups (also known farming systems groups) are integral to information dissemination with farmers, and other industry stakeholders. Objective: Research was conducted to investigate the use of digital tools for knowledge and information sharing by staff at grower groups by examining their perceptions about the implementation, intended audience, impact monitoring, and the facilitators and barriers associated with using digital tools. Methods: This case study involved semi-structured, in-depth interviews at three time points with staff at four grower groups located in four states in Australia to understand their perceptions about using digital tools for knowledge and information sharing with different industry audiences. Results and Conclusions: The findings demonstrate that grower groups are embracing digital technologies: they are applying digital tools broadly and monitoring their uptake closely for the dissemination of industry-specific knowledge and information sharing. Groups were found to be utilising multiple, digital tools and adopting a range of approaches that are regularly monitored for impact, while continuously refining the use of digital tools over time. Despite the broad adoption of digital tools, various challenges were identified, including low audience uptake with some digital methods, limited staffing capacity and expertise to implement digital tools, poor digital infrastructure and unreliable connectivity in remote regions. A set of guidelines to support the application of digital tools for engagement within the agriculture sector have been developed, informed by this research. They include the provisioning of activities for digital engagement for planning, audience engagement, content assessment, piloting and reviewing, and establishing communities of practice for shared learnings. Significance: This research provides important insights about the application of digital tools for knowledge and information sharing with stakeholders in the Australian grains industry, by grower groups. This case study highlights the overwhelming commitment of grower groups to adopt digital tools for sharing knowledge to a wide and varied audience. The adoption of digital tools was ubiquitous by grower groups however their application represents just one component of a broader strategy of information sharing that also incorporates non-digital approaches (paper-based; face-to-face) to meet the information delivery preferences of stakeholders. © 202

Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation

Knowledge distillation (KD) has widely been used for model compression and domain adaptation for speech applications. In the presence of multiple teachers, knowledge can easily be transferred to the student by averaging the models output. However, previous research shows that the student do not adapt well with such combination. This paper propose to use an elitist sampling strategy at the output of ensemble teacher models to select the best-decoded utterance generated by completely out-of-domain teacher models for generalizing unseen domain. The teacher models are trained on AMI, LibriSpeech and WSJ while the student is adapted for the Switchboard data. The results show that with the selection strategy based on the individual model’s posteriors the student model achieves a better WER compared to all the teachers and baselines with a minimum absolute improvement of about 8.4%. Furthermore, an insights on the model adaptation with out-of-domain data has also been studied via correlation analysis

Environmental constraints influencing survival of an African parasite in a north temperate habitat: effects of temperature on egg development

SUMMARYFactors affecting survival of parasites introduced to new geographical regions include changes in environmental temperature. Protopolystoma xenopodis is a monogenean introduced with the amphibian Xenopus laevis from South Africa to Wales (probably in the 1960s) where low water temperatures impose major constraints on life-cycle processes. Effects were quantified by maintenance of eggs from infections in Wales under controlled conditions at 10, 12, 15, 18, 20 and 25°C. The threshold for egg viability/ development was 15°C. Mean times to hatching were 22 days at 25°C, 32 days at 20°C, extending to 66 days at 15°C. Field temperature records provided calibration of transmission schedules. Although egg production continues year-round, all eggs produced during >8 months/ year die without hatching. Output contributing significantly to transmission is restricted to 10 weeks (May-mid-July). Host infection, beginning after a time lag of 8 weeks for egg development, is also restricted to 10 weeks (July-September). Habitat temperatures (mean 15·5°C in summer 2008) allow only a narrow margin for life-cycle progress: even small temperature increases, predicted with 'global warming', enhance infection. This system provides empirical data on the metrics of transmission permitting long-term persistence of isolated parasite populations in limiting environments

Predicting Impacts of Climate Change on Fasciola hepatica Risk

Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits