Although the concept of ecological integrity is referred in many country legislations there is no consensus on how to formalize and implement it. One possible definition is as the capacity of an ecosystem to support and maintain a balanced, integrated, and adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of a natural habitat of the region. Our objective is to model this interpretation of ecological integrity from a set of ecological measures that can be estimated from ecological inventory data.

Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI" , designed to safeguard humanity and its values. I will argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning" in a particle arrangement? What is "life"? What is the ultimate ethical imperative, i.e., how should we strive to rearrange the particles of our Universe and shape its future? If we fail to answer the last question rigorously, this future is unlikely to contain humans.

Under the auspices of “being green,” we have given our printed word over to a cyber-medium that cannot be touched or felt or folded. Our information is as volatile as the authority protecting our storage devices. Eliminating a book or changing its text can be done by literally pressing a button -- without a fire or an erasure marking, without smoke, without evidence. Our data is ephemeral along with the web in which we weave it. This paper considers the current ease of censorship, and that the non-permanence of data and links can wreak havoc on our societal infra-structure if the wrong entities (human or machine) with the wrong motives have the control to determine its fate.

The DNR is able to purchase lands and engage in conservation easements, but there is considerable uncertainty (for the Conservation reserve design is the problem of selecting reasons enumerated above) about which lands to target, and parcels of land such that the assembled set maximizes when. Furthermore, for any parcel that is protected through some criterion pertaining to the conservation of species or purchase or easement, DNR encumbers a responsibility to natural communities (Williams, ReVelle, and Levin 2005).

We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Such cost to serve analysis has application both strategically and operationally in transportation. The problem is formally given by the traveling salesperson game (TSG), a cooperative total utility game in which agents correspond to locations in a travelling salesperson problem (TSP). The cost to serve a location is an allocated portion of the cost of an optimal tour. The Shapley value is one of the most important normative division schemes in cooperative games, giving a principled and fair allocation both for the TSG and more generally. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and present the first proof that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we then develop six proxies for that value which are relatively easy to compute. We perform an experimental evaluation using Synthetic Euclidean games as well as games derived from real-world tours calculated for fast-moving consumer goods scenarios. Our experiments show that several computationally tractable allocation techniques correspond to good proxies for the Shapley value.

Bike sharing systems consist of a fleet of bikes placed in a network of docking stations. These bikes can then be rented and returned to any of the docking stations after usage. Predicting unrealized bike demand at locations currently without bike stations is important for effectively designing and expanding bike sharing systems. We predict pairwise bike demand for New York City’s Citi Bike system. Since the system is driven by daily commuters we focus only on the morning rush hours between 7:00 AM to 11:00 AM during weekdays. We use taxi usage, weather and spatial variables as covariates to predict bike demand, and further analyze the influence of precipitation and day of week. We show that aggregating stations in neighborhoods can substantially improve predictions. The presented model can assist planners by predicting bike demand at a macroscopic level, between pairs of neighborhoods.

Inspired by recent successes of deep learning in computer vision and speech recognition, we propose a novel framework to encode time series data as different types of images, namely, Gramian Angular Fields (GAF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for classification. Using a polar coordinate system, GAF images are represented as a Gramian matrix where each element is the trigonometric sum (i.e., superposition of directions) between different time intervals. MTF images represent the first order Markov transition probability along one dimension and temporal dependency along the other. We used Tiled Convolutional Neural Networks (tiled CNNs) on 12 standard datasets to learn high-level features from individual GAF, MTF, and GAF-MTF images that resulted from combining GAF and MTF representations into a single image. The classification results of our approach are competitive with five stateof-the-art approaches. An analysis of the features and weights learned via tiled CNNs explains why the approach works.

Cities and their employment catchment areas are focus points of economic activity, transportation, and social interactions. The need for land use and transport inte- grated modelling (LUTI modelling) as a decision aid tool in urban planning, has become apparent. Instanti- ating such models on cities, requires a substantial data collection, model structuring and parameter estimation effort; for conciseness, the latter is referred to here as calibration. This work is a partial effort towards the integrated calibration of LUTI models. It considers one of the most widely used LUTI models and softwares, Tranus. The usual calibration approach for Tranus is briefly reviewed. It is then reformulated as an optimisa- tion problem, in order to make it amenable to the sys- tematic incorporation of constraints on parameters and additional data and to form a clear basis for future fully integrated calibration. The problem at hand concerns a dynamic system; an approach is shown how to “elimi- nate” parts of the dynamics in order to ease the param- eter optimisation. We also discuss how to validate cali- bration results and propose to use synthetic data gener- ated from real world problems in order to assess conver- gence properties and accuracy of calibration methods.

A paradigm of case-supported principle-based behavior (CPB) is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles needed for ethical guidance of the behavior of autonomous systems. Such principles help ensure the ethical behavior of complex and dynamic systems and further serve as a basis for justification of their actions as well as a control abstraction for managing unanticipated behavior. The requirements, methods, implementation, and evaluation components of the CPB paradigm are detailed.

This is especially the media as an event unfolds. This methodology consists of case in emergency situations, where the spread of a false rumour three main steps: (i) collection of (source) tweets posted during can have dangerous consequences. For instance, in a an emergency situation, sampling in such a way that situation where a hurricane is hitting a region, or a terrorist it is manageable for human assessment, while generating attack occurs in a city, access to accurate information is a good number of rumourous tweets from multiple stories, crucial for finding out how to stay safe and for maximising (ii) collection of conversations associated with each of the citizens' wellbeing. This is even more important in cases source tweets, which includes a set of replies discussing the where users tend to pass on false information more often source tweet, and (iii) collection of human annotations on than real facts, as occurred with Hurricane Sandy in 2012 the tweets sampled. We provide a definition of a rumour (Zubiaga and Ji 2014). Hence, identifying rumours within a which informs the annotation process. Our definition draws social media stream can be of great help for the development on definitions from different sources, including dictionaries of tools that prevent the spread of inaccurate information.