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One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.
We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.
The northward movement and collision of the Arabian plate with Eurasia generates compressive stresses and resulting shortening in Iran. Within the Alborz Mountains, North Iran, a complex and not well understood system of strike-slip and thrust faults accomodates a fundamental part of the NNE-SSW oriented shortening. On 28th of May 2004 the Mw 6.3 Baladeh earthquake hit the north-central Alborz Mountains. It is one of the rare and large events in this region in modern time and thus a seldom chance to study earthquake mechanisms and the local ongoing deformation processes. It also demonstrated the high vulnerability of this densily populated region.
We present climatic consequences of the Representative Concentration Pathways (RCPs) using the coupled climate model CLIMBER-3 alpha, which contains a statistical-dynamical atmosphere and a three-dimensional ocean model. We compare those with emulations of 19 state-of-the-art atmosphere-ocean general circulation models (AOGCM) using MAGICC6. The RCPs are designed as standard scenarios for the forthcoming IPCC Fifth Assessment Report to span the full range of future greenhouse gas (GHG) concentrations pathways currently discussed. The lowest of the RCP scenarios, RCP3-PD, is projected in CLIMBER-3 alpha to imply a maximal warming by the middle of the 21st century slightly above 1.5 degrees C and a slow decline of temperatures thereafter, approaching today's level by 2500. We identify two mechanisms that slow down global cooling after GHG concentrations peak: The known inertia induced by mixing-related oceanic heat uptake; and a change in oceanic convection that enhances ocean heat loss in high latitudes, reducing the surface cooling rate by almost 50%. Steric sea level rise under the RCP3-PD scenario continues for 200 years after the peak in surface air temperatures, stabilizing around 2250 at 30 cm. This contrasts with around 1.3 m of steric sea level rise by 2250, and 2 m by 2500, under the highest scenario, RCP8.5. Maximum oceanic warming at intermediate depth (300-800 m) is found to exceed that of the sea surface by the second half of the 21st century under RCP3-PD. This intermediate-depth warming persists for centuries even after surface temperatures have returned to present-day values, with potential consequences for marine ecosystems, oceanic methane hydrates, and ice-shelf stability. Due to an enhanced land-ocean temperature contrast, all scenarios yield an intensification of monsoon rainfall under global warming.
In order to provide probabilistic projections of the future evolution of the Atlantic Meridional Overturning Circulation (AMOC), we calibrated a simple Stommel-type box model to emulate the output of fully coupled three-dimensional atmosphere-ocean general circulation models (AOGCMs) of the Coupled Model Intercomparison Project (CMIP). Based on this calibration to idealised global warming scenarios with and without interactive atmosphere-ocean fluxes and freshwater perturbation simulations, we project the future evolution of the AMOC mean strength within the covered calibration range for the lower two Representative Concentration Pathways (RCPs) until 2100 obtained from the reduced complexity carbon cycle-climate model MAGICC 6. For RCP3-PD with a global mean temperature median below 1.0 degrees C warming relative to the year 2000, we project an ensemble median weakening of up to 11% compared to 22% under RCP4.5 with a warming median up to 1.9 degrees C over the 21st century. Additional Greenland meltwater of 10 and 20 cm of global sea-level rise equivalent further weakens the AMOC by about 4.5 and 10 %, respectively. By combining our outcome with a multi-model sea-level rise study we project a dynamic sea-level rise along the New York City coastline of 4 cm for the RCP3-PD and of 8 cm for the RCP4.5 scenario over the 21st century. We estimate the total steric and dynamic sea-level rise for New York City to be about 24 cm until 2100 for the RCP3-PD scenario, which can hold as a lower bound for sea-level rise projections in this region, as it does not include ice sheet and mountain glacier contributions.
The dynamics of the North Atlantic subpolar gyre (SPG) are assessed under present and glacial boundary conditions by investigating the SPG sensitivity to surface wind-stress changes in a coupled climate model. To this end, the gyre transport is decomposed in Ekman, thermohaline, and bottom transports. Surface wind-stress variations are found to play an important indirect role in SPG dynamics through their effect on water-mass densities. Our results suggest the existence of two dynamically distinct regimes of the SPG, depending on the absence or presence of deep water formation (DWF) in the Nordic Seas and a vigorous Greenland-Scotland ridge (GSR) overflow. In the first regime, the GSR overflow is weak and the SPG strength increases with wind-stress as a result of enhanced outcropping of isopycnals in the centre of the SPG. As soon as a vigorous GSR overflow is established, its associated positive density anomalies on the southern GSR slope reduce the SPG strength. This has implications for past glacial abrupt climate changes, insofar as these can be explained through latitudinal shifts in North Atlantic DWF sites and strengthening of the North Atlantic current. Regardless of the ultimate trigger, an abrupt shift of DWF into the Nordic Seas could result both in a drastic reduction of the SPG strength and a sudden reversal in its sensitivity to wind-stress variations. Our results could provide insight into changes in the horizontal ocean circulation during abrupt glacial climate changes, which have been largely neglected up to now in model studies.
The thermal behavior of poly(methoxydiethylenglycol acrylate) (PMDEGA) is studied in thin hydrogel films on solid supports and is compared with the behavior in aqueous solution. The PMDEGA hydrogel film thickness is varied from 2 to 422 nm. Initially, these films are homogenous, as measured with optical microscopy, atomic force microscopy, X-ray reflectivity, and grazing-incidence small-angle X-ray scattering (GISAXS). However, they tend to de-wet when stored under ambient conditions. Along the surface normal, no long-ranged correlations between substrate and film surface are detected with GISAXS, due to the high mobility of the polymer at room temperature. The swelling of the hydrogel films as a function of the water vapor pressure and the temperature are probed for saturated water vapor pressures between 2,380 and 3,170 Pa. While the swelling capability is found to increase with water vapor pressure, swelling in dependence on the temperature revealed a collapse phase transition of a lower critical solution temperature type. The transition temperature decreases from 40.6 A degrees C to 36.6 A degrees C with increasing film thickness, but is independent of the thickness for very thin films below a thickness of 40 nm. The observed transition temperature range compares well with the cloud points observed in dilute (0.1 wt.%) and semi-dilute (5 wt.%) solution which decrease from 45 A degrees C to 39 A degrees C with increasing concentration.
We investigate concentrated solutions of poly(styrene-b-N-isopropyl acrylamide) (P(S-b-NIPAM)) diblock copolymers in deuterated water (D2O). Both structural changes and the changes of the segmental dynamics occurring upon heating through the lower critical solution temperature (LCST) of PNIPAM are studied using small-angle neutron scattering and neutron spin-echo spectroscopy. The collapse of the micellar shell and the cluster formation of collapsed micelles at the LCST as well as an increase of the segmental diffusion coefficient after crossing the LCST are detected. Comparing to our recent results on a triblock copolymer P(S-b-NIPAM-b-S) [25], we observe that the collapse transition of P(S-b-NIPAM) is more complex and that the PNIPAM segmental dynamics are faster than in P(S-b-NIPAM-b-S).
Process analysis usually focuses only on single and selected processes. It is either existent processes that are recorded and analysed or reference processes that are implemented. So far no evident effort has been put into generalising specific process aspects into patterns and comparing those patterns with regard to their efficiency and effectiveness. This article focuses on the combination of dynamic and holistic analytical elements in enterprise architectures. Our goal is to outline an approach to analyse the development of business processes in a cyclical matter and demonstrate this approach based on an existent modelling language. We want to show that organisational learning can derive from the systematic analysis of past and existent processes from which patterns of successful problem solving can be deducted.