The utility of species distribution models for applications in invasion and global change biology is critically dependent on their transferability between regions or points in time, respectively. We introduce two methods that aim to improve the transferability of presence-only models: density-based occurrence thinning and performance-based predictor selection. We evaluate the effect of these me...

We introduce a novel conditional e-cash protocol allowing future anonymous cashing of bank-issued e-money only upon the satisfaction of an agreedupon public condition. Payers are able to remunerate payees for services that depend on future, yet to be determined outcomes of events. Moreover, payees are able to further transfer payments to third parties. Once payment complete, any double-spending...

We regard a path as a train moving on a graph. A graph G is called n-transferable if any path of length n can be moved to any other such path by several steps. We will show that every planar graph with minimum degree at least three is at most 10 transferable.

We extend Jendrol’ and Skupień’s results about the local structure of maps on the 2-sphere: In this paper we show that if a polyhedral map G on a surface M of Euler characteristic χ(M) ≤ 0 has more than 126|χ(M)| vertices, then G has a vertex with ”nearly” non-negative combinatorial curvature. As a corollary of this, we can deduce that path transferability of such graphs are at most 12. keyword...

Castor bean and Jatropha contain seed oil of industrial importance, share taxonomical and biochemical similarities, which can be explored for identifying SSRs in the whole genome sequence of castor bean and utilized in Jatropha curcas. Whole genome analysis of castor bean identified 5,80,986 SSRs with a frequency of 1 per 680 bp. Genomic distribution of SSRs revealed that 27% were present in th...

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we ...