The Complexity of Order Type Isomorphism

The order type of a point set in $R^d$ maps each $(d{+}1)$-tuple of points to its orientation (e.g., clockwise or counterclockwise in $R^2$). Two point sets $X$ and $Y$ have the same order type if there exists a mapping $f$ from $X$ to $Y$ for which every $(d{+}1)$-tuple $(a_1,a_2,\ldots,a_{d+1})$ of $X$ and the corresponding tuple $(f(a_1),f(a_2),\ldots,f(a_{d+1}))$ in $Y$ have the same orientation. In this paper we investigate the complexity of determining whether two point sets have the same order type. We provide an $O(n^d)$ algorithm for this task, thereby improving upon the $O(n^{\lfloor{3d/2}\rfloor})$ algorithm of Goodman and Pollack (1983). The algorithm uses only order type queries and also works for abstract order types (or acyclic oriented matroids). Our algorithm is optimal, both in the abstract setting and for realizable points sets if the algorithm only uses order type queries.Comment: Preliminary version of paper to appear at ACM-SIAM Symposium on Discrete Algorithms (SODA14

The “systems approach” to treating the brain: opportunities in developmental psychopharmacology

The significance of early life for the long-term programming of mental health is increasingly being recognized. However, most psychotropic medications are currently intended for adult patients, and early psychopharmacological approaches aimed at reverting aberrant neurodevelopmental trajectories are missing. Psychopharmacologic intervention at an early age faces the challenge of operating in a highly plastic system and requires a comprehensive knowledge of neurodevelopmental mechanisms. Recently the systems biology approach has contributed to the understanding of neuroplasticity mechanisms from a new perspective that interprets them as the result of complex and dynamic networks of signals from different systems. This approach is creating opportunities for developmental psychopharmacology, suggesting novel targets that can modulate the course of development by interfering with neuroplasticity at an early age. We will discuss two interconnected systems—the immune and gut microbiota—that regulate neurodevelopment and that have been implicated in preclinical research as new targets in the prevention of aberrant brain development

The Basilicata Wealth Fund: Resource Policy and Long-Run Economic Development in Southern Italy

This paper contributes to the growing political economy literature of within-country natural resources management, by proposing a new resource policy for the oil-rich southern Italian region of Basilicata. The policy proposal is to establish a (regional) wealth fund in which all the royalty revenues from non-renewable natural resource exploitation in Basilicata would be stored and fully converted into low-risk financial assets. The scope is to give priority to long-run investments as to better exploit revenues from large-scale extraction of natural capital. Establishing a wealth fund at the regional sub-national level is a novel approach that can be applied to other resource-rich regions in the world. I label the fund as the Basilicata Wealth Fund (BWF). The BWF would be a regionally owned investment fund, however independently administered from national authorities (for instance, as an independent legal entity under the jurisdiction of the Bank of Italy). In addition, the paper posits a transparent and clear-cut spending fiscal rule in order to let regional authorities use the resource revenues to finance economic policy. The clear advantage from the BWF would be the stronger focus on long-run economic development and the higher accountability, hence avoiding misuse of resource revenues for myopic fiscal spending

Does community social embeddedness promote generalized trust? An experimental test of the spillover effect

Despite the theoretical relevance attributed to the spillover effect, little empirical research has focused on testing its causal validity. Addressing this gap in the literature, I propose a novel experimental design to test if the overall density of social links in a community promotes trustworthy and trusting behaviors with absolute strangers. Controlling for social integration (i.e. the individual number of social connections), I found that density fosters higher levels of trust. In particular, results show that people in denser communities are more likely to trust their unknown fellow citizens, encouraging isolated subjects to engage with strangers. However, evidence did not support the idea that community social embeddedness causes an increase of trustworthiness, indicating that the spillover effect works only with respect to trust

Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN

Entropy, Triangulation, and Point Location in Planar Subdivisions

A data structure is presented for point location in connected planar subdivisions when the distribution of queries is known in advance. The data structure has an expected query time that is within a constant factor of optimal. More specifically, an algorithm is presented that preprocesses a connected planar subdivision G of size n and a query distribution D to produce a point location data structure for G. The expected number of point-line comparisons performed by this data structure, when the queries are distributed according to D, is H + O(H^{2/3}+1) where H=H(G,D) is a lower bound on the expected number of point-line comparisons performed by any linear decision tree for point location in G under the query distribution D. The preprocessing algorithm runs in O(n log n) time and produces a data structure of size O(n). These results are obtained by creating a Steiner triangulation of G that has near-minimum entropy.Comment: 19 pages, 4 figures, lots of formula

A Static Optimality Transformation with Applications to Planar Point Location

Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is fine-tuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2D analogue of the jump from Knuth's optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information.Comment: 13 pages, 1 figure, a preliminary version appeared at SoCG 201

Spatial, seasonal and climatic predicitve models of Rift Valley Fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’