This article argues that currently the largest gap between human and machine learning is learning algorithms' inability to perform deep transfer, that is, generalize from one domain to another domain containing different objects, classes, properties and relations. We argue that second-order Markov logic is ideally suited for this purpose, and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, Web and social network domains.

Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers - that is, data markedly deviating from the postulated models. A variational counterpart to least-trimmed squares regression is shown closely related to an L0-(pseudo)norm-regularized estimator, that encourages sparsity in a vector explicitly modeling the outliers. This connection suggests efficient solvers based on convex relaxation, which lead naturally to a variational M-type estimator equivalent to the least-absolute shrinkage and selection operator (Lasso). Outliers are identified by judiciously tuning regularization parameters, which amounts to controlling the sparsity of the outlier vector along the whole robustification path of Lasso solutions. Reduced bias and enhanced generalization capability are attractive features of an improved estimator obtained after replacing the L0-(pseudo)norm with a nonconvex surrogate. The novel robust spline-based smoother is adopted to cleanse load curve data, a key task aiding operational decisions in the envisioned smart grid system. Computer simulations and tests on real load curve data corroborate the effectiveness of the novel sparsity-controlling robust estimators.

Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner, became a basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to first-order causal logic.

The development of discursive knowledge presumes the communication of meaning as analytically different from the communication of information. Knowledge can then be considered as a meaning which makes a difference. Whereas the communication of information is studied in the information sciences and scientometrics, the communication of meaning has been central to Luhmann's attempts to make the theory of autopoiesis relevant for sociology. Analytical techniques such as semantic maps and the simulation of anticipatory systems enable us to operationalize the distinctions which Luhmann proposed as relevant to the elaboration of Husserl's "horizons of meaning" in empirical research: interactions among communications, the organization of meaning in instantiations, and the self-organization of interhuman communication in terms of symbolically generalized media such as truth, love, and power. Horizons of meaning, however, remain uncertain orders of expectations, and one should caution against reification from the meta-biological perspective of systems theory.

Trust is an approach to managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a simple approach to reasoning about trust with logi

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

We examine the performance of Total Variation (TV) smoothing for processing of noisy Electrocardiogram (ECG) recorded by an ambulatory device. The TV smoothing is compared with traditionally-used band-pass filtering using ECG with artificially added noise, as well as with real-world noise obtained during physiological monitoring. The fundamental difference between TV smoothing and traditional band-pass filtering is that TV smoothing allow preserving sharp slopes in the ECG, while traditional smoothing dampens them. Since the QRS complex represents a feature with steep slopes, the TV smoothing is a better choice ECG filtering. We found that TV smoothing outperforms traditional filtering on ECG signals recorded under different conditions and can be used as effective filtering tool in popular QRS detection algorithms.

If computational social science is to find practical application in informing policy decisions and proportionately analyzing courses of action, then it will have to make progress in the area of composition of social models.  Since a single simulation cannot hold a world of information, policy makers need to switch in and out modules in federations of simulations to test policies against all possible social environments.  Voting processes as they occur in nature, both in the form of cognition in a human mind of disparate world views, and in the form of equilibria seeking coevolution of species, inform how to combine model results externally and deeply, respectively.  These algorithms, which use the same principles of soft computation found in nature, enable any models to mesh together, even if they have different ontologies, or their data conflict, regardless of the degree they overlap.  A whiteboard architecture in which models report in their own ontologies how other models may inform them and what they have to offer other models, is a framework for the arbitrary meshing of social models.

Many approaches have been introduced for representing and solving multiagent coordination problems. Unfortunately, these methods make assumptions that limit their usefulness when combined with human operators and real-life hardware and software. In this paper, we discuss the problem of using agents in conjunction with human operators to improve coordination as well as possible models that could be used in these problems. Our approach — Space Collaboration via an Agent Network (SCAN) — enables proxy agents to represent each of the stakeholder agencies in a space setting and shows how the SCAN agent network could facilitate collaboration by identifying opportunities and methods of collaboration. We discuss this approach as well as the challenges in extending models to 1) take advantage of human input, 2) deal with the limited and uncertain information that will be present and 3) combat the scalability issues in solution methods for a large number of decentralized agents. As a first step toward providing rich models for these domains, we describe a method to bound the solution quality due to bounded model uncertainty.

There is an urgent need for interventions to assist teenagers and young adults in appreciating the physical and social risks of binge drinking. While research on the health risks associated with alcohol abuse is well developed, the translation and communication of this knowledge to young people is not. This paper describes a prototype visualisation tool, an Alcohol Risk Calculator, that provides personalised information on risks associated with alcohol consumption based on individual drinking habits. Its design is informed by studies of graphical literacy, evidence on forms of presenting risk that aid understanding, and theory that provides insight into changing health damaging behaviour.