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Deep learning is a sub-field of machine learning that has recently gained substantial popularity in various domains such as computer vision, automatic speech recognition, natural language processing, and bioinformatics. Deep-learning techniques are able to learn complex feature representations from raw signals and thus also have potential to improve signal processing in the context of brain-computer interfaces (BCIs). However, they typically require large amounts of data for training - much more than what can often be provided with reasonable effort when working with brain activity recordings of any kind. In order to still leverage the power of deep-learning techniques with limited available data, special care needs to be taken when designing the BCI task, defining the structure of the deep model, and choosing the training method. This chapter presents example approaches for the specific scenario of music-based brain-computer interaction through electroencephalography - in the hope that these will prove to be valuable in different settings as well. We explain important decisions for the design of the BCI task and their impact on the models and training techniques that can be used. Furthermore, we present and compare various pre-training techniques that aim to improve the signal-to-noise ratio. Finally, we discuss approaches to interpret the trained models.
From precipitation to damage
(2018)
Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale.
Germany has a long tradition of federalism extending far back in history (Ziblatt 2004; Broschek 2011). This tradition has always been characterized by a discrepancy between the attitudes of the public to its federalism and the reform ideas of the (political) elites. While the public has a strong desire for an equality of living conditions, solidarity, social cohesion, and cooperation between the orders of government, academic discourse is shaped by calls for wide-ranging federalism reforms, which are oriented toward the American model of "dual federalism." Against this background, this chapter contrasts public attitudes on key aspects of the federal system with long-lasting academic recommendations for reform. Light will be shed on the general perception of the federal system as a whole, the division of powers, and in particular the issue of joint decision-making (Politikverflechtung) between the orders of government-all issues that have been repeatedly interrogated in various surveys. A further aspect of these polls is the question of the extent to which solidarity or competition shall be realized between the federal and Land governments-a question that is highly controversial in politics and academia (especially in the fiscal equalization debate), though public perceptions are quite different.
Recent studies by Bastiaanse and colleagues found that time reference is selectively impaired in people with nonfluent agrammatic aphasia, with reference to the past being more difficult to process than reference to the present or to the future. To account for this dissociation, they formulated the PAst DIscourse LInking Hypothesis (PADILIH), which posits that past reference is more demanding than present/future reference because it involves discourse linking. There is some evidence that this hypothesis can be applied to people with fluent aphasia as well. However, the existing evidence for the PADILIH is contradictory, and most of it has been provided by employing a test that predominantly taps retrieval processes, leaving largely unexplored the underlying ability to encode time reference-related prephonological features. Within a cross-linguistic approach, this study tests the PADILIH by means of a sentence completion task that 'equally' taps encoding and retrieval abilities. This study also investigates if the PADILIH’s scope can be extended to fluent aphasia. Greek- and Italian-speaking individuals with aphasia participated in the study. The Greek group consisted of both individuals with nonfluent agrammatic aphasia and individuals with fluent aphasia, who also presented signs of agrammatism. The Italian group consisted of individuals with agrammatic nonfluent aphasia only. The two Greek subgroups performed similarly. Neither language group of participants with aphasia exhibited a pattern of performance consistent with the predictions of the PADILIH. However, a double dissociation observed within the Greek group suggests a hypothesis that may reconcile the present results with the PADILIH.
Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Niño-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an ‘uncertainty-aware’ framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.
Purpose:
To test whether the negative relationship between perceived stress and quality of life (Hypothesis 1) can be buffered by perceived social support in patients with dementia as well as in caregivers individually (Hypothesis 2: actor effects) and across partners (Hypothesis 3: partner effects and actor-partner effects).
Method:
A total of 108 couples (N = 216 individuals) comprised of one individual with early-stage dementia and one caregiving partner were assessed at baseline and one month apart. Moderation effects were investigated by applying linear mixed models and actor-partner interdependence models.
Results:
Although the stress-quality of life association was more pronounced in caregivers (beta = -.63, p<.001) compared to patients (beta= -.31, p<.001), this association was equally moderated by social support in patients (beta = .14, p<.05) and in the caregivers (beta =.13, p<.05). From one partner to his or her counterpart, the partner buffering and actor-partner-buffering effect were not present.
Conclusion:
The stress-buffering effect has been replicated in individuals with dementia and caregivers but not across partners. Interventions to improve quality of life through perceived social support should not only focus on caregivers, but should incorporate both partners.
Background: Flooding during seasonal monsoons affects millions of hectares of rice-cultivated areas across Asia. Submerged rice plants die within a week due to lack of oxygen, light and excessive elongation growth to escape the water. Submergence tolerance was first reported in an aus-type rice landrace, FR13A, and the ethylene-responsive transcription factor (TF) gene SUB1A-1 was identified as the major tolerance gene. Intolerant rice varieties generally lack the SUB1A gene but some intermediate tolerant varieties, such as IR64, carry the allelic variant SUB1A-2. Differential effects of the two alleles have so far not been addressed. As a first step, we have therefore quantified and compared the expression of nearly 2500 rice TF genes between IR64 and its derived tolerant near isogenic line IR64-Sub1, which carries the SUB1A-1 allele. Gene expression was studied in internodes, where the main difference in expression between the two alleles was previously shown. Results: Nineteen and twenty-six TF genes were identified that responded to submergence in IR64 and IR64-Sub1, respectively. Only one gene was found to be submergence-responsive in both, suggesting different regulatory pathways under submergence in the two genotypes. These differentially expressed genes (DEGs) mainly included MYB, NAC, TIFY and Zn-finger TFs, and most genes were downregulated upon submergence. In IR64, but not in IR64-Sub1, SUB1B and SUB1C, which are also present in the Sub1 locus, were identified as submergence responsive. Four TFs were not submergence responsive but exhibited constitutive, genotype-specific differential expression. Most of the identified submergence responsive DEGs are associated with regulatory hormonal pathways, i.e. gibberellins (GA), abscisic acid (ABA), and jasmonic acid (JA), apart from ethylene. An in-silico promoter analysis of the two genotypes revealed the presence of allele-specific single nucleotide polymorphisms, giving rise to ABRE, DRE/CRT, CARE and Site II cis-elements, which can partly explain the observed differential TF gene expression. Conclusion: This study identified new gene targets with the potential to further enhance submergence tolerance in rice and provides insights into novel aspects of SUB1A-mediated tolerance.
Inland salt meadows are particularly valuable ecosystems, because they support a variety of salt-adapted species (halophytes). They can be found throughout Europe; including the peatlands of the glacial lowlands in northeast Germany. These German ecosystems have been seriously damaged through drainage. To assess and ultimately limit the damages, temporal monitoring of soil salinity is essential, which can be conducted by geoelectrical techniques that measure the soil electrical conductivity. However, there is limited knowledge on how to interpret electrical conductivity surveys of peaty salt meadows. In this study, temporal and spatial monitoring of dissolved salts was conducted in saline peatland soils using different geoelectrical techniques at different scales (1D: conductivity probe, 2D: conductivity cross-sections). Cores and soil samples were taken to validate the geoelectrical surveys. Although the influence of peat on bulk conductivity is large, the seasonal dynamics of dissolved salts within the soil profile could be monitored by repeated geoelectrical measurements. A close correlation is observed between conductivity (similar to salinity) at different depths and temperature, precipitation and corresponding groundwater level. The conductivity distribution between top- and subsoil during the growing season reflected the leaching of dissolved salts by precipitation and the capillary rise of dissolved salts by increasing temperature (similar to evaporation). Groundwater levels below 0.38 cm resulted in very low conductivities in the topsoil, which is presumably due to limited soil moisture and thus precipitation of salts. Therefore, to prevent the disappearance of dissolved salts from the rooting zone, which are essential for the halophytes, groundwater levels should be adjusted to maintain depths of between 20 and 35 cm. Lower groundwater levels will lead to the loss of dissolved salts from the rooting zone and higher levels to increasing dilution with fresh rainwater. The easy-to-handle conductivity probe is an appropriate tool for salinity monitoring. Using this probe with regressions adjusted for sandy and organic substrates (peat and organic gyttja) additional influences on bulk conductivity (e.g. cation exchange capacity, water content) can be compensated for and the correlation between salinity and electrical conductivity is high.