Jason Jingshi Li

Sensing the air we breathe: the opensense Zurich dataset

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.Molecular oxygen (O 2 )is a basic requirement for cellular growth and viability and many aspects of anatomy and physiology are dedicated to achieving reliable distribution. Recent work has identified a specific sensing and response system, centred around a transcription complex called Hypoxia-inducible Factor 1 (HIF-1), which forms the focus of this review. The HIF-system operates in all cell types and modulates a very broad range of cellular pathways, consistent with the broad importance of oxygen. It is implicated in a rapidly expanding range of developmental, physiological and pathological settings, and is potentially relevant to almost all areas of clinical medicine. Excitingly, the pathway can be activated with low molecular weight compounds which should offer therapeutic benefit, especially in diseases where oxygen supply is compromised.

Incentive Mechanisms for Community Sensing

Sensing and monitoring of our natural environment are important for sustainability. As sensor systems grow to a large scale, it will become infeasible to place all sensors under centralized control. We investigate community sensing, where sensors are controlled by self-interested agents that report their measurements to a center. The center can control the agents only through incentives that motivate them to provide the most accurate and useful reports. We consider different game-theoretic mechanisms that provide such incentives and analyze their properties. As an example, we consider an application of community sensing for monitoring air pollution.

Decomposition and tractability in qualitative spatial and temporal reasoning

Constraint networks in qualitative spatial and temporal reasoning (QSTR) typically feature variables defined on infinite domains. Mainstream algorithms for deciding network consistency are based on searching for network refinements whose consistency is known to be tractable, either directly or by using a SAT solver. Consequently, these algorithms treat all networks effectively as complete graphs, and are not directly amenable to complexity bounds based on network structure, such as measured by treewidth, that are well known in the finite-domain case. The present paper makes two major contributions, spanning both theory and practice. First, we identify a sufficient condition under which consistency can be decided in polynomial time for networks of bounded treewidth in QSTR, and show that this condition is satisfied by a range of calculi including the Interval Algebra, Rectangle Algebra, Block Algebra, RCC8, and RCC5. Second, we apply the techniques used in establishing these results to a SAT encoding of QSTR, and obtain a new, more compact encoding which is also guaranteed to be solvable in polynomial time for networks of bounded treewidth, and which leads to a significant advance of the state of the art in solving the hardest benchmark problems.

Eliciting truthful measurements from a community of sensors

As the Internet of Things grows to large scale, its components will increasingly be controlled by self-interested agents. For example, sensor networks will evolve to community sensing where a community of agents combine their data into a single coherent structure. As there is no central quality control, agents need to be incentivized to provide accurate measurements. We propose game-theoretic mechanisms that provide such incentives and show their application on the example of community sensing for monitoring air pollution. These mechanisms can be applied to most sensing scenarios and allow the Internet of Things to grow to much larger scale than currently exists.

Restarts and Nogood Recording in Qualitative Constraint-based Reasoning

This paper introduces restart and nogood recording techniques in the domain of qualitative spatial and temporal reasoning. Nogoods and restarts can be applied orthogonally to usual methods for solving qualitative constraint satisfaction problems. In particular, we propose a more general definition of nogoods that allows for exploiting information about nogoods and tractable subclasses during backtracking search. First evaluations of the proposed techniques show promising results.

On Finding Approximate Solutions of Qualitative Constraint Networks

Qualitative Spatial and Temporal Reasoning (QSTR) represents spatial and temporal information in terms of human comprehensible qualitative predicates and reasons about qualitative information by solving qualitative constraint networks (QCNs). Despite significant progress in the past three decades, more and more evidence has shown that it is inherently hard to find exact solutions for expressive qualitative constraints. In many applications, however, we are often required to make decisions in a very limited time. In these cases, finding a good approximate solution in seconds is much more desirable than waiting days for an exact solution. In this paper, we will exploit the algebraic structure of qualitative calculi (e.g. Interval Algebra and RCC8) as well as their conceptual neighbourhood graphs to develop approximate methods for consistency checking in QSTR. Moreover, we propose and empirically compare four independent methods to serve as tools for finding good approximate solutions for the given qualitative calculi.

The Coherence of Theories-Dependencies and Weights

One way to evaluate and compare rival but potentially incompatible theories that account for the same set of observations is coherence. In this paper we take the quantitative notion of theory coherence as proposed by Kwok et al. (Proceedings of the Fifth Pacific Rim Conference on Artificial Intelligence, pp. 553–564, 1998) and broaden its foundations. The generalisation will give a measure of the efficacy of a sub-theory as against single theory components. This also gives rise to notions of dependencies and couplings to account for how theory components interact with each other. Secondly we wish to capture the fact that not all components within a theory are of equal importance. To do this we assign weights to theory components. This framework is applied to game theory and the performance of a coherentist player is investigated within the iterated Prisoner’s Dilemma.

Implementing Modal Tableaux Using Sentential Decision Diagrams

A Sentential Decision Diagram (SDD) is a novel representation of a boolean function which contains a Binary Decision Diagram (BDD) as a subclass. Previous research suggests that BDDs are effective in implementing tableaux-based automated theorem provers. We investigate whether SDDs can offer improved efficiency when used in the same capacity. Preliminarily, we found that SDDs compile faster than BDDs only on large CNF formulae. In general, we found the BDD-based modal theorem prover still outperforms our SDD-based modal theorem prover. However, the SDD-based approach excels over the BDD-based approach in a select subset of benchmarks that have large sizes and modalities that are less nested or fewer in number.

Evaluating cases in legal disputes as rival theories

In this paper we propose to draw a link from the quantitative notion of coherence, previously used to evaluate rival scientific theories, to legal reasoning. We evaluate the stories of the plaintiff and the defendant in a legal case as rival theories by measuring how well they cohere when accounting for the evidence. We show that this gives rise to a formalized comparison between rival cases that account for the same set of evidence, and provide a possible explanation as to why judgements may favour one side over the other. We illustrate our approach by applying it to a known legal dispute from the literature.