MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.

From Each According to Means, to Each According to Needs? Distributional Effects of Abolishing Asset-Based Payments for Residential Care in Austria

BackgroundMost countries in Europe require out-of-pocket payments (OPPs) for nursing homes based on users’ income and often assets. This was also the case in Austria until 2018 when asset-based contributions to residential care —denoted the ‘Pflegeregress’ – were abolished, leaving a shortfall in revenue. We aim to determine how the Pflegeregress was distributed across different groups in Austria prior to 2018, what the distributional consequences of its abolishment were, and what the distributional impact of different financing alternatives would be.MethodsCircumventing data availability issues, we construct a micro-simulation model using a matched administrative dataset on residential care users receiving the Austrian care allowance (Pflegegeldinformation, PFIF, HVB, and Pflegedienstleistungsstatistik, Statistik Austria) and survey data (SHARE, wave 6). Using this model, we estimate the expected duration of residential care and OPPs under the Pflegeregress of a representative sample of older people aged 65+ in Austria, as well as OPPs under budgetary neutral financing alternatives to the abolished asset-based contribution, namely an inheritance tax and a social insurance scheme. The distributional impact of abolishing the Pflegeregress and these alternative scenarios is assessed through a number of measures, such as ability to pay, Concentration Indices (CI) and a needs-standardized measure.ResultsWe find that lower income individuals and homeowners disproportionately contributed to asset-based OPPs for residential care prior to 2018 due in large part to their higher use of residential care and the low asset-exemption thresholds. These groups were therefore the largest beneficiaries of its abolishment. The alternative financing scenarios tested would result in a more progressive distribution of payments (i.e. concentrated on more affluent individuals). ConclusionOur findings indicate the limited ability of asset-based OPPs to target those with higher assets, thus questioning the fairness of these instruments for financing residential care facilities for older people in Austria. Findings also suggest that the parameterization of such OPPs (such as asset exemption thresholds) and patterns of residential care use are key variables for assessing the distribution of asset-based OPPs for residential care use. Policy alternatives that decouple payments from use would entail greater transfers from healthy to less healthier individuals.

The Deep-time Digital Earth program: data-driven discovery in geosciences

Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR principles (findable, accessible, interoperable, and reusable); we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-time Digital Earth (DDE) program initiated by the International Union of Geological Sciences (IUGS) and developed in cooperation with national geological surveys, professional associations, academic institutions, and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge, and facilitate data-driven discovery in the understanding of Earth's evolution.

An Adaptive-Data-Driven Attack Detection Framework on ADS-B Data

<p>With the variety and quantity of flights increasing, accurate and efficient surveillance methods are in great demands for the next generation air traffic management. Relying on high accuracy, wide coverage, low deployment cost and data share support, Automatic Dependent Surveillance – Broadcast (ADS-B) is becoming the primary surveillance method in 2020. However, ADS-B data is lacking of sufficient security measures to ensure data integrity and authentication, which makes it face with various attack threats. To detect the malicious data caused by attack behaviours accurately, an adaptive-data-driven attack detection framework is proposed, which is utilized to establish the consistent framework for predictive discriminant detection methods. It is composed of sequential predictor, behaviour discriminator and dynamic updater, enhancing adaptive sequential detection performances. According to the framework, an effective implementation is designed to improve attack detection accuracy: (I) The sequential predictor identifies flight phases to predict sequential data effectively and accomplish model fusion to generate ADS-B predictive data sequences. (II) The behaviour discriminator utilizes value differences and contextual information to distinguish attack data from ADS-B data sequences. (III) The dynamic updater is designed to update the training data sets and discriminate threshold dynamically, improving the adaptation in face of concept drifts for ADS-B data. By experiments on real ADS-B data with diverse attack patterns, the feasibility and efficiency of the framework are validated.</p>

An Adaptive-Data-Driven Attack Detection Framework on ADS-B Data

<p>With the variety and quantity of flights increasing, accurate and efficient surveillance methods are in great demands for the next generation air traffic management. Relying on high accuracy, wide coverage, low deployment cost and data share support, Automatic Dependent Surveillance – Broadcast (ADS-B) is becoming the primary surveillance method in 2020. However, ADS-B data is lacking of sufficient security measures to ensure data integrity and authentication, which makes it face with various attack threats. To detect the malicious data caused by attack behaviours accurately, an adaptive-data-driven attack detection framework is proposed, which is utilized to establish the consistent framework for predictive discriminant detection methods. It is composed of sequential predictor, behaviour discriminator and dynamic updater, enhancing adaptive sequential detection performances. According to the framework, an effective implementation is designed to improve attack detection accuracy: (I) The sequential predictor identifies flight phases to predict sequential data effectively and accomplish model fusion to generate ADS-B predictive data sequences. (II) The behaviour discriminator utilizes value differences and contextual information to distinguish attack data from ADS-B data sequences. (III) The dynamic updater is designed to update the training data sets and discriminate threshold dynamically, improving the adaptation in face of concept drifts for ADS-B data. By experiments on real ADS-B data with diverse attack patterns, the feasibility and efficiency of the framework are validated.</p>

Web-CDI: A system for online administration of the MacArthur-Bates Communicative Development Inventories

Understanding the mechanisms that drive variation in children’s language acquisition requires large, population-representative datasets of children’s word learning across development. Parent report measures such as the MacArthur-Bates Communicative Development Inventories (CDI) are commonly used to collect such data, but the traditional paper-based forms make the curation of large datasets logistically challenging. Many CDI datasets are thus gathered using convenience samples, often recruited from communities in proximity to major research institutions. Here, we introduce Web-CDI, a web-based tool which allows researchers to collect CDI data online. Web-CDI contains functionality to collect and manage longitudinal data, share links to test administrations, and download vocabulary scores. To date, over 3,500 valid Web-CDI administrations have been completed. General trends found in past norming studies of the CDI are present in data collected from Web-CDI: scores of children’s productive vocabulary grow with age, female children show a slightly faster rate of vocabulary growth, and participants with higher levels of educational attainment report slightly higher vocabulary production scores than those with lower levels of education attainment. We also report results from an effort to oversample non-white, lower-education participants via online recruitment (N = 241). These data showed similar demographic trends to the full sample but this effort resulted in a high exclusion rate. We conclude by discussing implications and challenges for the collection of large, population-representative datasets.

How Ideas from Ecological Capture-Recapture Models May Inform Multiple Systems Estimation Analyses

Abundance estimation, for both human and animal populations, informs policy decisions and population management. Capture-recapture and multiple sources data share a common structure; the population can be partially enumerated and individuals are identifiable. Consequently, the analytical methods were developed simultaneously. However, whilst ecological models have been developed to describe highly complex, biologically realistic scenarios, for example modeling population changes through time and combining different forms of data, multiple systems estimation has changed comparatively less so. In this paper we provide a brief description of the historical development of ecological and epidemiological capture-recapture and discuss the associated underlying differences that have led to model divergence. We identify three key areas where ecological modeling methods may inform and improve multiple systems estimation.