U.S. Law of the Sea Cruise to Map the Foot of the Slope of the Northeast U.S. Atlantic Continental Margin: Leg 6

U.S. Law of the Sea Cruise to Map the Foot of the Slope of the Northeast U.S. Atlantic Continental Margin: Leg 6 Cruise KNOX17RR May 1 – 31, 2008 Ft. Lauderdale, FL to Woods Hole, M

Data-driven network alignment

Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional knowledge between the aligned nodes. However, current NA methods do not end up aligning functionally related nodes. A likely reason is that they assume it is topologically similar nodes that are functionally related. However, we show that this assumption does not hold well. So, a paradigm shift is needed with how the NA problem is approached. We redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity, like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns. We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance

Contextual emergence of intentionality

By means of an intriguing physical example, magnetic surface swimmers, that can be described in terms of Dennett's intentional stance, I reconstruct a hierarchy of necessary and sufficient conditions for the applicability of the intentional strategy. It turns out that the different levels of the intentional hierarchy are contextually emergent from their respective subjacent levels by imposing stability constraints upon them. At the lowest level of the hierarchy, phenomenal physical laws emerge for the coarse-grained description of open, nonlinear, and dissipative nonequilibrium systems in critical states. One level higher, dynamic patterns, such as, e.g., magnetic surface swimmers, are contextually emergent as they are invariant under certain symmetry operations. Again one level up, these patterns behave apparently rational by selecting optimal pathways for the dissipation of energy that is delivered by external gradients. This is in accordance with the restated Second Law of thermodynamics as a stability criterion. At the highest level, true believers are intentional systems that are stable under exchanging their observation conditions.Comment: 27 pages; 4 figures (Fig 1. Copyright by American Physical Society); submitted to Journal of Consciousness Studie

Subjective Experiences of Space and Time: Self, Sensation, and Phenomenal Time

The investigation of subjective experiences (SEs) of space and time is at the core of consciousness research. The term ‘space’ includes the subject and objects. The SE of subject, I-ness, is defined as ‘Self’. The SEs of objects, subject’s external body, and subject’s internal states such as feelings, thoughts, and so on can be investigated using the proto-experience (PE)-SE framework. The SE of time is defined as ‘phenomenal time’ (which includes past, present and future) and the SE of space as ‘phenomenal space’. The three non-experiential materialistic models are as follows: (I) The quantum-dissipation model [25] can connect the discrete neural signals to classical electromagnetic field to ‘quantum field theory and chaos theory’ for explaining memory. (II) The soliton-catalytic model [8] hypothesizes that all living processes including micro- and macro-processes can be explained by catalysis process. (III) The ‘sensation from evolution of action’ model [13] proposes that SEs are internalized during evolution. All these models can address to some extent the function of structures, such as perception. They cannot address explanatory gap. The complementary experiential PE-SE framework [37] addresses this psycho-physical gap and elucidates the SEs of space and time

On the fine structure of the quiet solar \Ca II K atmosphere

We investigate the morphological, dynamical, and evolutionary properties of the internetwork and network fine structure of the quiet sun at disk centre. The analysis is based on a $\sim$6 h time sequence of narrow-band filtergrams centred on the inner-wing \Ca II K$_{\rm 2v}$ reversal at 393.3 nm. The results for the internetwork are related to predictions derived from numerical simulations of the quiet sun. The average evolutionary time scale of the internetwork in our observations is 52 sec. Internetwork grains show a tendency to appear on a mesh-like pattern with a mean cell size of $\sim$4-5 arcsec. Based on this size and the spatial organisation of the mesh we speculate that this pattern is related to the existence of photospheric downdrafts as predicted by convection simulations. The image segmentation shows that typical sizes of both network and internetwork grains are in the order of 1.6 arcs.Comment: 8 pages, 9 figure