Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary search method is used to find the best network hyperparameters. We then use this optimised topology of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37% accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36) accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit a higher starting classification accuracy. The significance of this work is due to the successful transfer of ability between models trained on two different biological signal domains, reducing the need for building more computationally complex models in future research

Synthetic Biological Signals Machine-generated by GPT-2 improve the Classification of EEG and EMG through Data Augmentation

Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and with a scarcity of training samples. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Those can rarely be generalised to the whole population and appear to over complicate simple action recognition such as grasp and release (standard actions in robotic prosthetics and manipulators). We show for the first time that multiple GPT-2 models can machine-generate synthetic biological signals (EMG and EEG) and improve real data classification. Models trained solely on GPT-2 generated EEG data can classify a real EEG dataset at 74.71% accuracy and models trained on GPT-2 EMG data can classify real EMG data at 78.24% accuracy. Synthetic and calibration data are then introduced within each cross validation fold when benchmarking EEG and EMG models. Results show algorithms are improved when either or both additional data are used. A Random Forest achieves a mean 95.81% (1.46) classification accuracy of EEG data, which increases to 96.69% (1.12) when synthetic GPT-2 EEG signals are introduced during training. Similarly, the Random Forest classifying EMG data increases from 93.62% (0.8) to 93.9% (0.59) when training data is augmented by synthetic EMG signals. Additionally, as predicted, augmentation with synthetic biological signals also increases the classification accuracy of data from new subjects that were not observed during training. A Robotiq 2F-85 Gripper was finally used for real-time gesture-based control, with synthetic EMG data augmentation remarkably improving gesture recognition accuracy, from 68.29% to 89.5%

Palladium Complexes Derived from Waste as Catalysts for C-H Functionalisation and C-N Bond Formation

Three-way catalysts (TWCs) are widely used in vehicles to convert the exhaust emissions from internal combustion engines into less toxic pollutants. After around 8–10 years of use, the declining catalytic activity of TWCs causes them to need replacing, leading to the generation of substantial amounts of spent TWC material containing precious metals, including palladium. It has previously been reported that [NnBu4]2[Pd2I6] is obtained in high yield and purity from model TWC material using a simple, inexpensive and mild reaction based on tetrabutylammonium iodide in the presence of iodine. In this contribution, it is shown that, through a simple ligand exchange reaction, this dimeric recovery complex can be converted into PdI2(dppf) (dppf = 1,1′-bis(diphenylphosphino)ferrocene), which is a direct analogue of a commonly used catalyst, PdCl2(dppf). [NnBu4]2[Pd2I6] displayed high catalytic activity in the oxidative functionalisation of benzo[h]quinoline to 10-alkoxybenzo[h]quinoline and 8-methylquinoline to 8-(methoxymethyl)quinoline in the presence of an oxidant, PhI(OAc)2. Near-quantitative conversions to the desired product were obtained using a catalyst recovered from waste under milder conditions (50 °C, 1–2 mol% Pd loading) and shorter reaction times (2 h) than those typically used in the literature. The [NnBu4]2[Pd2I6] catalyst could also be recovered and re-used multiple times after the reaction, providing additional sustainability benefits. Both [NnBu4]2[Pd2I6] and PdI2(dppf) were also found to be active in Buchwald–Hartwig amination reactions, and their performance was optimised through a Design of Experiments (DoE) study. The optimised conditions for this waste-derived palladium catalyst (1–2 mol% Pd loading, 3–6 mol% of dppf) in a bioderived solvent, cyclopentyl methyl ether (CPME), offer a more sustainable approach to C-N bond formation than comparable amination protocols

Look and listen:A multi-modality late fusion approach to scene classification for autonomous machines

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16, 000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach

An architecture for the autonomic curation of crowdsourced knowledge

Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to providing numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality

A process-based model of conifer forest structure and function with special emphasis on leaf lifespan

We describe the University of Sheffield Conifer Model (USCM), a process-based approach for simulating conifer forest carbon, nitrogen, and water fluxes by up-scaling widely applicable relationships between leaf lifespan and function. The USCM is designed to predict and analyze the biogeochemistry and biophysics of conifer forests that dominated the ice-free high-latitude regions under the high pCO2 “greenhouse” world 290–50 Myr ago. It will be of use in future research investigating controls on the contrasting distribution of ancient evergreen and deciduous forests between hemispheres, and their differential feedbacks on polar climate through the exchange of energy and materials with the atmosphere. Emphasis is placed on leaf lifespan because this trait can be determined from the anatomical characteristics of fossil conifer woods and influences a range of ecosystem processes. Extensive testing of simulated net primary production and partitioning, leaf area index, evapotranspiration, nitrogen uptake, and land surface energy partitioning showed close agreement with observations from sites across a wide climatic gradient. This indicates the generic utility of our model, and adequate representation of the key processes involved in forest function using only information on leaf lifespan, climate, and soils

New constraints on atmospheric CO2 concentration for the Phanerozoic

Earth's atmospheric CO2 concentration (ca) for the Phanerozoic Eon is estimated from proxies and geochemical carbon cycle models. Most estimates come with large, sometimes unbounded uncertainty. Here, we calculate tightly constrained estimates of ca using a universal equation for leaf gas exchange, with key variables obtained directly from the carbon isotope composition and stomatal anatomy of fossil leaves. Our new estimates, validated against ice cores and direct measurements of ca, are less than 1000 ppm for most of the Phanerozoic, from the Devonian to the present, coincident with the appearance and global proliferation of forests. Uncertainties, obtained from Monte Carlo simulations, are typically less than for ca estimates from other approaches. These results provide critical new empirical support for the emerging view that large (~2000-3000 ppm), long-term swings in ca do not characterize the post-Devonian and that Earth's long-term climate sensitivity to ca is greater than originally thought. Key Points A novel CO2 proxy calculates past atmospheric CO2 with improved certainty CO2 is unlikely to have exceeded ~1000 ppm for extended periods post Devonian Earth's long-term climate sensitivity to CO2 is greater than originally thought

Hydrating softwood and hardwood samples using pure and modified supercritical carbon dioxide

This article describes an in-progress research project that looks to investigate the use of supercritical carbon dioxide (scCO2) for the addition of water to historic and modern, softwood and hardwood samples. The experiments were carried out at 20 MPa and 50 °C, the effects of co-solvent addition, methanol (MeOH), were examined. A three point bend test provided mechanical data for the wood samples treated with both pure and modified scCO2. All the wood samples, with only one exception, saw an increase in Modulus of Rupture (MOR) after being treated with scCO2. Thereby indicating an increased resistance to force in the treated samples. Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectroscopy was performed to help deduce if any trends in the OH/CH and OH/Cellulose peak area ratios could be established with the nature of the treatment and the type of wood used. The development of this technique seeks to be relevant and safe for applications within modern conservation practices, where dry and fragile materials are prevalent

Gene diversity in grevillea populations introduced in Brazil and its implication on management of genetic resources.

A variabilidade isoenzimática para seis populações de Grevillea robusta, oriundas de um teste de procedências/progenies, implantado no delineamento em blocos casualizados com 5 plantas por parcela, no Sul do Brasil, é descrita. A estrutura genética da população foi analisada utilizando-se marcadores bioquímicos, aos 5 anos de idade, especificamente para os locos MDH-3, PGM-2, DIA-2, PO-1, PO-2, SOD-1, e SKDH-1. As procedências do norte de ocorrência natural (Rathdowney e Woodenbong) apresentaram divergência genética superior, em relação à média das progênies, considerando o número de alelos por locus, (Ap), a riqueza alélica (Rs), a diversidade genética de Nei (H), e o coeficiente de endogamia (f). A endogamia foi detectada em diversos graus. A testemunha comercial apresentou o maior coeficiente de endogamia, (f = 0,4448), comparativamente à média das procedências (f = 0,2306), possivelmente devido à insuficiente amostragem populacional na região de origem (Austrália). Apesar de sua ocorrência natural restrita, observou-se correlação positiva entre divergência genética e distância geográfica entre as populações originais. A distância genética e análise de cluster, baseada no modelo bayesiano, mostrou três grupos de procedências distintos: 1) Rathdowney- QLD e Woodenbong-QLD; 2) Paddy?s Flat-NSW; e 3) Mann River-NSW, Boyd River-NSW e a testemunha comercial (material utilizado no Brasil). O agrupamento da testemunha com as procedências Mann River-NSW e Boyd River-NSW sugere um maior potencial das procedências do norte para o melhoramento genético visando à produção de madeira no Brasil, devido a sua elevada diversidade genética e baixo coeficiente de endogamia