Social and asocial learning in collective action problems:the rise and fall of socially-beneficial behaviour

The allocation of common-pool resources is an important topic in technical and socio-Technical systems, and when left unmanaged, such systems often collapse to highly unequal and unsustainable outcomes. Recent work has highlighted a role for electronic institutions in managing such resources, to ensure socially-beneficial outcomes in the long term. However, open self-organising multi-Agent systems often involve agents that learn behaviours in order to meet their goals. In this paper we explore the interplay between institutional features and forms of social and asocial learning employed by self-interested agents. We show that, while recent results have associated social learning with sustainability, this is sensitive to the form of social learning used. We show that more realistic models that combine social and asocial learning are more likely to lead to unsustainable institutions and anti-social outcomes. However, a key role for pardons in the sanction mechanism of the institution is identified, which allows for tolerance of a range of behaviours associated with ongoing learning, including complacency and exploration

Long-Range Excitation of Collective Modes in Mesoscopic Metal Clusters

We develop a semiclassical theory for the long range excitation of plasmon resonances in atomic clusters, based on the doorway hypothesis. The effect of the width of the plasmon resonance is fully taken into account. As an application we study plasmon excitation in small Sodium clusters, in collisions with electrons and protons.Comment: 18 pages, 4 figure

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

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

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

Recovery of dialysis patients with COVID-19 : health outcomes 3 months after diagnosis in ERACODA

Background. Coronavirus disease 2019 (COVID-19)-related short-term mortality is high in dialysis patients, but longer-term outcomes are largely unknown. We therefore assessed patient recovery in a large cohort of dialysis patients 3 months after their COVID-19 diagnosis. Methods. We analyzed data on dialysis patients diagnosed with COVID-19 from 1 February 2020 to 31 March 2021 from the European Renal Association COVID-19 Database (ERACODA). The outcomes studied were patient survival, residence and functional and mental health status (estimated by their treating physician) 3 months after COVID-19 diagnosis. Complete follow-up data were available for 854 surviving patients. Patient characteristics associated with recovery were analyzed using logistic regression. Results. In 2449 hemodialysis patients (mean ± SD age 67.5 ± 14.4 years, 62% male), survival probabilities at 3 months after COVID-19 diagnosis were 90% for nonhospitalized patients (n = 1087), 73% for patients admitted to the hospital but not to an intensive care unit (ICU) (n = 1165) and 40% for those admitted to an ICU (n = 197). Patient survival hardly decreased between 28 days and 3 months after COVID-19 diagnosis. At 3 months, 87% functioned at their pre-existent functional and 94% at their pre-existent mental level. Only few of the surviving patients were still admitted to the hospital (0.8-6.3%) or a nursing home (∼5%). A higher age and frailty score at presentation and ICU admission were associated with worse functional outcome. Conclusions. Mortality between 28 days and 3 months after COVID-19 diagnosis was low and the majority of patients who survived COVID-19 recovered to their pre-existent functional and mental health level at 3 months after diagnosis

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Welcome! Creating an Effective New Employee Orientation Program at Kansas State Libraries

K-State Libraries found itself in the interesting position of a simultaneous hiring boom and organizational redesign that threatened to leave a large group of new employees adrift without guidance from an HR director or unit. Deciding to embrace change from within, an ad hoc task force of three stepped forward to create, implement, and manage a new employee orientation program until an HR director could be hired. We started with formal and informal surveys of staff members to assess needs. Based on those results, and with the endorsement of the Libraries’ leadership team, we created a three-pronged program to orient incoming faculty and staff to the Libraries. The program was designed as a whole-organization orientation, with the intention of standardizing “first month” experiences on the assumption that employees who start off on the right foot will be more likely to adapt, succeed, and be retained as contributing members of the organization. First of the three prongs was a step-by-step checklist for use by the administrative staff and immediate supervisor during the first 3 months of employment. This checklist covered basic necessities like phone lines and computer equipment, as well as orientations to other library departments and information about benefits, policies, and procedures. Next was an orientation notebook for the new employee, containing helpful campus information, documentation for common computer tasks, and general facts about the Libraries. Finally, we solicited and trained volunteer guides to be matched with each new employee. Guides were assigned from outside the new employee’s immediate work area to serve as a social connection/introduction to the rest of the library, and to be a friendly, neutral source for answering procedural questions. As of this writing, 17 individuals have been through the orientation program, and it has resulted in a smoother integration of these individuals into the Libraries compared to those hired before the orientation program was in place