Abstract:
Deforestation is rapidly transforming primary forests across the tropics into human-dominated landscapes. Consequently, conservationists need to understand how different taxa respond and adapt to these changes in order to develop appropriate management strategies. Our two year study seeks to determine how wild Sumatran orangutans (Pongo abelii) adapt to living in an isolated agroforest landscape by investigating the sex of crop-raiders related to population demographics, and their temporal variations in feeding behaviour and dietary composition. From focal animal sampling we found that nine identified females raided cultivated fruits more than the four males. Seasonal adaptations were shown through orangutan feeding habits that shifted from being predominantly fruit-based (56% of the total feeding time, then 22% on bark) to the fallback food of bark (44%, then 35% on fruits), when key cultivated resources such as jackfruit (Artocarpus integer), were unavailable. Cultivated fruits were mostly consumed in the afternoon and evening, when farmers had returned home. The finding that females take greater crop-raiding risks than males differs from previous human-primate conflict studies, probably because of the low risks associated (as farmers rarely retaliated) and low intraspecific competition between males. Thus, the behavioral ecology of orangutans living in this human-dominated landscape differs markedly from that in primary forest, where orangutans have a strictly wild food diet, even where primary rainforests directly borders farmland. The importance of wild food availability was clearly illustrated in this study with 21% of the total orangutan feeding time being allocated to feeding on cultivated fruits. As forests are increasingly converted to cultivation, humans and orangutans are predicted to come into conflict more frequently. This study reveals orangutan adaptations for coexisting with humans, e.g. changes in temporal foraging patterns, which should be used for guiding the development of specific human-wildlife conflict mitigation strategies to lessen future crop-raiding and conflicts.

Abstract:
We demonstrate that under certain conditions a theory of conformal quantum mechanics will exhibit the symmetries of two half-Virasoro algebras. We further demonstrate the conditions under which these algebras combine to form a single Virasoro algebra, and comment on the connection between this result and the AdS/CFT correspondence.

Abstract:
Spacetime as we know and love it is lost in most approaches to quantum gravity. For many of these approaches, as inchoate and incomplete as they may be, one of the main challenges is to relate what they take to be the fundamental non-spatiotemporal structure of the world back to the classical spacetime of GR. The present essay investigates how spacetime is lost and how it may be regained in one major approach to quantum gravity, loop quantum gravity.

Abstract:
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures with an unknown number of layers. To relate performance data gathered for different architectures, we define a new kernel for conditional parameter spaces that explicitly includes information about which parameters are relevant in a given structure. We show that this kernel improves model quality and Bayesian optimization results over several simpler baseline kernels.

Abstract:
In this book the authors introduce the notion of DSm vector spaces of refined labels. They also realize the refined labels as a plane and a n-dimensional space. Further, using these refined labels, several algebraic structures are defined. Finally DSm semivector space or refined labels is described. Authors also propose some research problems.

Abstract:
Sturmian words are infinite binary words with many equivalent definitions: They have a minimal factor complexity among all aperiodic sequences; they are balanced sequences (the labels 0 and 1 are as evenly distributed as possible) and they can be constructed using a mechanical definition. All this properties make them good candidates for being extremal points in scheduling problems over two processors. In this paper, we consider the problem of generalizing Sturmian words to trees. The problem is to evenly distribute labels 0 and 1 over infinite trees. We show that (strongly) balanced trees exist and can also be constructed using a mechanical process as long as the tree is irrational. Such trees also have a minimal factor complexity. Therefore they bring the hope that extremal scheduling properties of Sturmian words can be extended to such trees, as least partially. Such possible extensions are illustrated by one such example.

Abstract:
The role of unique node identifiers in network computing is well understood as far as symmetry breaking is concerned. However, the unique identifiers also leak information about the computing environment - in particular, they provide some nodes with information related to the size of the network. It was recently proved that in the context of local decision, there are some decision problems such that (1) they cannot be solved without unique identifiers, and (2) unique node identifiers leak a sufficient amount of information such that the problem becomes solvable (PODC 2013). In this work we give study what is the minimal amount of information that we need to leak from the environment to the nodes in order to solve local decision problems. Our key results are related to scalar oracles $f$ that, for any given $n$, provide a multiset $f(n)$ of $n$ labels; then the adversary assigns the labels to the $n$ nodes in the network. This is a direct generalisation of the usual assumption of unique node identifiers. We give a complete characterisation of the weakest oracle that leaks at least as much information as the unique identifiers. Our main result is the following dichotomy: we classify scalar oracles as large and small, depending on their asymptotic behaviour, and show that (1) any large oracle is at least as powerful as the unique identifiers in the context of local decision problems, while (2) for any small oracle there are local decision problems that still benefit from unique identifiers.

Abstract:
Let M be an n-manifold, and let A be a space with a partial sum behaving as an n-fold loop sum. We define the space C(M;A) of configurations in M with summable labels in A via operad theory. Some examples are symmetric products, labelled configuration spaces, and spaces of rational curves. We show that C(I^n,dI^n;A) is an n-fold delooping of C(I^n;A), and for n=1 it is the classifying space by Stasheff. If M is compact, parallelizable, and A is path connected, then C(M;A) is homotopic to the mapping space Map(M,C(I^n,dI^n;A)).

Abstract:
Reactive systems (RSs) represent a meta-framework aimed at deriving behavioral congruences for those computational formalisms whose operational semantics is provided by reduction rules. RSs proved a flexible specification device, yet so far most of the efforts dealing with their behavioural semantics focused on idem pushouts (IPOs) and saturated (also known as dynamic) bisimulations. In this paper we introduce a novel, intermediate behavioural equivalence: L-bisimilarity, which is able to recast both its IPO and saturated counterparts. The equivalence is parametric with respect to a set L of RSs labels, and it is shown that under mild conditions on L it is indeed a congruence. Furthermore, L-bisimilarity can also recast the notion of barbed semantics for RSs, proposed by the same authors in a previous paper. In order to provide a suitable test-bed, we instantiate our proposal by addressing the semantics of (asynchronous) CCS and of the calculus of mobile ambients.

Abstract:
This paper considers the challenge of evaluating a set of classifiers, as done in shared task evaluations like the KDD Cup or NIST TREC, without expert labels. While expert labels provide the traditional cornerstone for evaluating statistical learners, limited or expensive access to experts represents a practical bottleneck. Instead, we seek methodology for estimating performance of the classifiers which is more scalable than expert labeling yet preserves high correlation with evaluation based on expert labels. We consider both: 1) using only labels automatically generated by the classifiers (blind evaluation); and 2) using labels obtained via crowdsourcing. While crowdsourcing methods are lauded for scalability, using such data for evaluation raises serious concerns given the prevalence of label noise. In regard to blind evaluation, two broad strategies are investigated: combine & score and score & combine methods infer a single pseudo-gold label set by aggregating classifier labels; classifiers are then evaluated based on this single pseudo-gold label set. On the other hand, score & combine methods: 1) sample multiple label sets from classifier outputs, 2) evaluate classifiers on each label set, and 3) average classifier performance across label sets. When additional crowd labels are also collected, we investigate two alternative avenues for exploiting them: 1) direct evaluation of classifiers; or 2) supervision of combine & score methods. To assess generality of our techniques, classifier performance is measured using four common classification metrics, with statistical significance tests. Finally, we measure both score and rank correlations between estimated classifier performance vs. actual performance according to expert judgments. Rigorous evaluation of classifiers from the TREC 2011 Crowdsourcing Track shows reliable evaluation can be achieved without reliance on expert labels.