Piggybacking on an Autonomous Hauler: Business Models Enabling a System-of-Systems Approach to Mapping an Underground Mine

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote-controlled and autonomous haulers will operate underground guided by LiDAR sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Boliden's Kankberg mine, we propose establishing a system-of-systems (SoS) with LIDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.Comment: Preprint of industry track paper accepted for the 25th IEEE International Conference on Requirements Engineering (RE'17

Percolation transitions in the survival of interdependent agents on multiplex networks, catastrophic cascades, and SOS

The "SOS" in the title does not refer to the international distress signal, but to "solid-on-solid" (SOS) surface growth. The catastrophic cascades are those observed by Buldyrev {\it et al.} in interdependent networks, which we re-interpret as multiplex networks with agents that can only survive if they mutually support each other, and whose survival struggle we map onto an SOS type growth model. This mapping not only reveals non-trivial structures in the phase space of the model, but also leads to a new and extremely efficient simulation algorithm. We use this algorithm to study interdependent agents on duplex Erd\"os-R\'enyi (ER) networks and on lattices with dimensions 2, 3, 4, and 5. We obtain new and surprising results in all these cases, and we correct statements in the literature for ER networks and for 2-d lattices. In particular, we find that $d=4$ is the upper critical dimension, that the percolation transition is continuous for $d\leq 4$ but -- at least for $d\neq 3$ -- not in the universality class of ordinary percolation. For ER networks we verify that the cluster statistics is exactly described by mean field theory, but find evidence that the cascade process is not. For $d=5$ we find a first order transition as for ER networks, but we find also that small clusters have a nontrivial mass distribution that scales at the transition point. Finally, for $d=2$ with intermediate range dependency links we propose a scenario different from that proposed in W. Li {\it et al.}, PRL {\bf 108}, 228702 (2012).Comment: 19 pages, 32 figure

Estimating Network Kinetics of the MAPK/ERK Pathway Using Biochemical Data

The MAPK/ERK pathway is a major signal transduction system which regulates many fundamental cellular processes including the growth control and the cell death. As a result of these roles, it has a crucial importance in cancer as well as normal developmental processes. Therefore, it has been intensively studied resulting in a wealth of knowledge about its activation. It is also well documented that the activation kinetics of the pathway is crucial to determine the nature of the biological response. However, while individual biochemical steps are well characterized, it is still difficult to predict or even understand how the activation kinetics works. The aim of this paper is to estimate the stochastic rate constants of the MAPK/ERK network dynamics. Accordingly, taking a Bayesian approach, we combined underlying qualitative biological knowledge in several competing dynamic models via sets of quasireactions and estimated the stochastic rate constants of these reactions. Comparing the resulting estimates via the BIC and DIC criteria, we chose a biological model which includes EGFR degradation—Raf-MEK-ERK cascade without the involvement of RKIPs.

An Integrated Assessment of Climate Change, Air Pollution, and Energy Security Policy

This article presents an integrated assessment of climate change, air pollution, and energy security policy. Basis of our analysis is the MERGE model, designed to study the interaction between the global economy, energy use, and the impacts of climate change. For our purposes we expanded MERGE with expressions that quantify damages incurred to regional economies as a result of air pollution and lack of energy security. One of the main findings of our cost-benefit analysis is that energy security policy alone does not decrease the use of oil: global oil consumption is only delayed by several decades and oil reserves are still practically depleted before the end of the 21st century. If, on the other hand, energy security policy is integrated with optimal climate change and air pollution policy, the world’s oil reserves will not be depleted, at least not before our modeling horizon well into the 22nd century: total cumulative demand for oil then decreases by about 20%. More generally, we demonstrate that there are multiple other benefits of combining climate change, air pollution, and energy security policies and exploiting the possible synergies between them. These benefits can be large: for Europe the achievable CO2 emission abatement and oil consumption reduction levels are significantly deeper for integrated policy than when a strategy is adopted in which one of the three policies is omitted. Integrated optimal energy policy can reduce the number of premature deaths from air pollution by about 14,000 annually in Europe and over 3 million per year globally, by lowering the chronic exposure to ambient particulate matter. Only the optimal strategy combining the three types of energy policy can constrain the global average atmospheric temperature increase to a limit of 3ºC with respect to the pre-industrial level.Climate Change, Air Pollution, Energy Security, Cost-Benefit Analysis

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that together produce the opposite conclusion. These include training on sentence pairs rather than phrase pairs, averaging states to represent sequences, and regularizing aggressively. These improve LSTMs in both transfer learning and supervised settings. We also introduce a new recurrent architecture, the Gated Recurrent Averaging Network, that is inspired by averaging and LSTMs while outperforming them both. We analyze our learned models, finding evidence of preferences for particular parts of speech and dependency relations.Comment: Published as a long paper at ACL 201

Version control of pathway models using XML patches

<p>Background: Computational modelling has become an important tool in understanding biological systems such as signalling pathways. With an increase in size complexity of models comes a need for techniques to manage model versions and their relationship to one another. Model version control for pathway models shares some of the features of software version control but has a number of differences that warrant a specific solution.</p> <p>Results: We present a model version control method, along with a prototype implementation, based on XML patches. We show its application to the EGF/RAS/RAF pathway.</p> <p>Conclusion: Our method allows quick and convenient storage of a wide range of model variations and enables a thorough explanation of these variations. Trying to produce these results without such methods results in slow and cumbersome development that is prone to frustration and human error.</p&gt

Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work that was accepted for publication in Knowledge-Based System