Chiral Modification of the Tetrakis(pentafluorophenyl)borate Anion with Myrtanyl Groups

The synthesis and characterization of chiral [B(C6F5)4]– derivatives bearing a myrtanyl group instead of a fluoro substituent in the para position are described. These new chiral borates were isolated as their bench‐stable lithium, sodium, and cesium salts. The corresponding trityl salts were prepared and tested as catalysts in representative counteranion‐directed Diels–Alder reactions and Mukaiyama aldol additions but no enantioselectivity was obtained. Preformation of a chalcone‐derived silylcarboxonium ion with the chiral borate as counteranion did not lead to any asymmetric induction in a reaction with cyclohexa‐1,3‐diene.TU Berlin, Open-Access-Mittel - 201

Distance and T-square sampling for spatial measures of tree diversity

Distance sampling and its statistically improved variant, T-square sampling, are important sampling methods in plant ecology. They have often been applied in the context of plant density estimations and are comparatively easy to implement, since they intuitively follow the nearest-neighbour principle and thus do not require the layout of sample plots. Previous research studying distance sampling suggested that T-square sampling may also lead to an improved estimation of spatial tree diversity indices. We simulated distance and T-square sampling in six large fully mapped forest areas for seven tree diversity indices of which some competed for the same diversity aspect, i.e. tree location (dispersion), tree species and tree size diversity. Our results demonstrated that both distance and T-square sampling are indeed robust methods for sampling spatial measures of tree diversity. The sample size required for a sampling error of 10% does not exceed 20% of the total number of trees in a sampling area. Tsquare sampling has the ability to adapt to different spatial patterns of tree locations and this ability is key to the way the method controls estimation bias. The sample size required for species mingling and size differentiation clearly depends on the underlying spatial tree pattern in the sampling area. With most diversity indices, sample size reductions between 0.06% and 40% could be achieved by the application of T-square sampling compared to traditional distance sampling. All other conditions being equal, we could identify the uniform angle index, the species mingling index and the size differentiation index as those diversity indices achieving lower sampling error values than their competitors. For tree density estimations the Diggle and Byth estimators performed best. Based on our results, T-square sampling can be considered a robust sampling method for spatial tree diversity indices that is easy to apply in the field

The Time of Liberation: Angela Davis\u27s Prison Abolition and Giorgio Agamben\u27s Coming Community

The project explores the ethical, social, and political subject of incarceration. I investigate Angela Davis’s multifaceted critique of the prison industrial complex – focusing primarily on the tenets of racism, classism, and capitalism – and take an interdisciplinary approach to advancing her call for prison abolition by way of Giorgio Agamben’s radical adjustments to traditional discourses about ontology in his work The Coming Community. Agamben’s rendering of ontology in terms of impotentiality and indifference, when put in dialogue with Davis, exposes latent and unexplored philosophic suggestions Davis is making – specifically regarding a non-normative interpretation of temporality and an operation of liberation best understood as indefinite rather than finite and attainable. Ultimately, the poetic re-thinking Agamben applies to ontology and its political consequences serve as one blueprint for the kind of cognitive re-orientation vital for the prison abolitionist project: abolishing the conditions which allow for the prison industrial complex to be an unquestioned, inevitable part of social reality. Experimenting with thinkers that have seemingly disparate concerns and styles creates a space for more imaginative approaches to potentially mitigating limited, oppressive modes of thought, practices, and institutions

Unravelling the mechanisms of spatial correlation between species and size diversity in forest ecosystems

With ongoing climate change at global scale we are currently losing biodiversity at an unprecedented rate. The insurance hypothesis and associated research, however, suggest that biodiversity has a major stabilising effect in ecosystems. In this situation, it is crucial to develop a better understanding of natural processes of maintaining biodiversity for employing them in conservation practice. In forest ecosystems, spatial species and size diversity are important aspects of α-diversity at woodland community and species population level. Both aspects of spatial diversity stem from complex relationships between tree interaction, disturbances and subsequent waves of colonisation by tree seedlings of various species. Using point process statistics, particularly the mark mingling function and the mark variogram, we studied the processes causing spatial correlations of species and size diversity. We found that spatial species dispersal and conspecific size distributions are key drivers of spatial species-size correlations and that a combination of simple random size-labelling techniques applied to mark variograms is instrumental in efficiently diagnosing them. If size ranges differ between species, spatial size diversity is largely a function of spatial species mingling. The existence of these correlations is crucial to conservation because they imply that conservation efforts can be rationalised: It is possible to focus on only one of the two tree diversity aspects. Interestingly, in multi-species forest ecosystems, although general species diversity is high, spatial species-size correlations can be diluted, because some of the many species populations may have similar size distributions

An Empirical Study of Perfect Potential Heuristics

Potential heuristics are weighted functions over state features of a planning task. A recent study defines the complexity of a task as the minimum required feature complexity for a potential heuristic that makes a search backtrack-free. This gives an indication of how complex potential heuristics need to be to achieve good results in satisficing planning. However, these results do not directly transfer to optimal planning. In this paper, we empirically study how complex potential heuristics must be to represent the perfect heuristic and how close to perfect heuristics can get with a limited number of features. We aim to identify the practical trade-offs between size, complexity and time for the quality of potential heuristics. Our results show that, even for simple planning tasks, finding perfect potential heuristics might be harder than expected

Abstraction Heuristics, Cost Partitioning and Network Flows

Cost partitioning is a well-known technique to make admissible heuristics for classical planning additive. The optimal cost partitioning of explicit-state abstraction heuristics can be computed in polynomial time with a linear program, but the size of the model is often prohibitive. We study this model from a dual perspective and develop several simplification rules to reduce its size. We use these rules to answer open questions about extensions of the state equation heuristic and their relation to cost partitioning

Higher-Dimensional Potential Heuristics for Optimal Classical Planning

Potential heuristics for state-space search are defined as weighted sums over simple state features. Atomic features consider the value of a single state variable in a factored state representation, while binary features consider joint assignments to two state variables. Previous work showed that the set of all admissible and consistent potential heuristics using atomic features can be characterized by a compact set of linear constraints. We generalize this result to binary features and prove a hardness result for features of higher dimension. Furthermore, we prove a tractability result based on the treewidth of a new graphical structure we call the context-dependency graph. Finally, we study the relationship of potential heuristics to transition cost partitioning. Experimental results show that binary potential heuristics are significantly more informative than the previously considered atomic ones