There is empirical evidence suggesting that a person's family, friends, or social ties influence who a person votes for. Sokhey & McClurg (2012) find that as political disagreement in a person's social network increases, then a person is less likely to vote correctly. We develop a model where voters have different favorite policies and wish to vote correctly for the candidate whose favorite policy is closest to their own. Voters have beliefs about each candidate's favorite policy which may or may not be correct. Voters update their beliefs about political candidates based on who their conservative and liberal social ties are supporting. We find that if everyone's social network consists only of those most like themselves, then the conditions needed for correct voting to be stable are fairly weak; thus political agreement in one's social network facilitates correct voting. We also give conditions under which correct voting is stable for networks exhibiting homophily and for networks exhibiting random social interactions.

Recent research indicates that knowledge about social networks can be leveraged to increase efficiency of interventions (Valente, 2012). However, in many settings, there exists considerable uncertainty regarding the structure of the network. This can render the estimation of potential effects of network-based interventions difficult, as providing appropriate guidance to select interventions often requires a representation of the whole network. In order to make use of the network property estimates to simulate the effect of interventions, it may be beneficial to sample networks from an estimated posterior predictive distribution, which can be specified using a wide range of models. Sampling networks from a posterior predictive distribution of network properties ensures that the uncertainty about network property parameters is adequately captured. The tendency for relationships among network properties to exhibit sharp thresholds has important implications for understanding global network topology in the presence of uncertainty; therefore, it is essential to account for uncertainty. We provide detail needed to sample networks for the specific network properties of degree distribution, mixing frequency, and clustering. Our methods to generate networks are demonstrated using simulated data and data from the National Longitudinal Study of Adolescent Health.

A formalization of Gödel’s incompleteness theorems using the Isabelle proof assistant is described. This is apparently the first mechanical verification of the second incompleteness theorem. The work closely follows Świerczkowski (2003), who gave a detailed proof using hereditarily finite set theory. The adoption of this theory is generally beneficial, but it poses certain technical issues that do not arise for Peano arithmetic. The formalization itself should be useful to logicians, particularly concerning the second incompleteness theorem, where existing proofs are lacking in detail.

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

Based on product of exponentials (POE) formula, three explicit error models are given in this paper for kinematic calibration of serial robot through measuring its end-effector positions. To obtain these error models, the tool frame should be chosen as reference frame at first, and then each position–error-related segment in the error models using pose measurement should be selected. And during kinematic parameter identification, all the errors in joint twists are identifiable, and the initial transformation errors and the joint zero-position errors can be identified conditionally. Namely, the initial transformation errors are identifiable if they do not contain orientation errors. And the joint zero-position errors are identifiable when a robot only consists of prismatic joints and the coordinates of its joint twists are linearly independent.

The effectiveness of this calibration method has been validated by simulations and experiments. The results show that: (1) the identification algorithms are robust and practical. (2) The method of position measurement is superior to that of pose measurement.

Accurate contour tracking is one of the main tasks in modern manufacturing processes. By considering coupling effects among multiple axes, this paper proposes a cross-coupled proportional-integral-derivative (PID) control developed in position domain, and the controller is applied to a multi-axis computer numerical control (CNC) machine for contour tracking performance improvement. Stability analysis is conducted for the developed position domain cross-coupled PID control using the Lyapunov method, and guidelines for the selection of control gains are provided. The contour tracking performance are improved compared to an equivalent time domain controller, since the reference axis in position domain control does not contribute any error to the overall contouring error of the system. Simulation results demonstrate the effectiveness of cross-coupled PID position domain control for both linear and circular contour tracking, and prove the robustness of the controller to deal with random disturbances. It also shows that position domain cross-coupled PID control provides better contour tracking performance over position domain PID control and the equivalent time domain PID control.

As a contribution to an eventual solution of the problem of the determination of the maximal subgroups of the Monster we prove that the Monster does not contain any subgroup isomorphic to $\mathrm{PSL}_2(27)$ .

Supplementary materials are available with this article.

In this paper, we present a theoretical study on the control of a compass gait walker using energy regulation between steps. We use a return map to relate the mid-stance robot kinetic energy between steps with two control inputs, namely, foot placement and ankle push-off. We show that by regulating robot kinetic energy between steps using the two control inputs, we are able to (1) generate a wide range of walking speeds and stride lengths, including average human walking; (2) cancel the effect of external disturbance fully in a single step (dead-beat control); and (3) switch from one periodic gait to another in a single step. We hope that insights from this control methodology can help develop robust controllers for practical bipedal robots.

In this investigation we explore a general strategy for constructing modal theories where the modal notion is conceived as a predicate. The idea of this strategy is to develop modal theories over axiomatic theories of truth. In this first paper of our two part investigation we develop the general strategy and then apply it to the axiomatic theory of truth Friedman-Sheard. We thereby obtain the theory Modal Friedman-Sheard. The theory Modal Friedman-Sheard is then discussed from three different perspectives. First, we show that Modal Friedman-Sheard preserves theoremhood modulo translation with respect to modal operator logic. Second, we turn to semantic aspects and develop a modal semantics for the newly developed theory. Third, we investigate whether the modal predicate of Modal Friedman-Sheard can be understood along the lines of a proposal of Kripke, namely as a truth predicate modified by a modal operator.

We describe algorithms that allow the computation of fundamental domains in the Bruhat–Tits tree for the action of discrete groups arising from quaternion algebras. These algorithms are used to compute spaces of rigid modular forms of arbitrary even weight, and we explain how to evaluate such forms to high precision using overconvergent methods. Finally, these algorithms are applied to the calculation of conjectural equations for the canonical embedding of p-adically uniformizable rational Shimura curves. We conclude with an example in the case of a genus 4 Shimura curve.

In this second and last paper of the two part investigation on “Modality and Axiomatic Theories of Truth” we apply a general strategy for constructing modal theories over axiomatic theories of truth to the theory Kripke-Feferman. This general strategy was developed in the first part of our investigation. Applying the strategy to Kripke-Feferman leads to the theory Modal Kripke-Feferman which we discuss from the three perspectives that we had already considered in the first paper, where we discussed the theory Modal Friedman-Sheard. That is, we first show that Modal Kripke-Feferman preserves theoremhood modulo translation with respect to modal operator logic. Second, we develop a modal semantics fitting the newly developed theory. Third, we investigate whether the modal predicate of Modal Kripke-Feferman can be understood along the lines of a proposal of Kripke, namely as a truth predicate modified by a modal operator.

Let $Q(N;q,a)$ be the number of squares in the arithmetic progression $qn+a$ , for $n=0$ , $1,\ldots,N-1$ , and let $Q(N)$ be the maximum of $Q(N;q,a)$ over all non-trivial arithmetic progressions $qn + a$ . Rudin’s conjecture claims that $Q(N)=O(\sqrt{N})$ , and in its stronger form that $Q(N)=Q(N;24,1)$ if $N\ge 6$ . We prove the conjecture above for $6\le N\le 52$ . We even prove that the arithmetic progression $24n+1$ is the only one, up to equivalence, that contains $Q(N)$ squares for the values of $N$ such that $Q(N)$ increases, for $7\le N\le 52$ ( $N=8,13,16,23,27,36,41$ and $52$ ).

Supplementary materials are available with this article.

Some of the most interesting and important results concerning quantum finite automata arethose showing that they can recognize certain languages with (much) less resources thancorresponding classical finite automata. This paper shows three results of such a typethat are stronger in some sense than other ones because (a) they deal with models ofquantum finite automata with very little quantumness (so-called semi-quantum one- andtwo-way finite automata); (b) differences, even comparing with probabilistic classicalautomata, are bigger than expected; (c) a trade-off between the number of classical andquantum basis states needed is demonstrated in one case and (d) languages (or the promiseproblem) used to show main results are very simple and often explored ones in automatatheory or in communication complexity, with seemingly little structure that could beutilized.

We show that if a Barker sequence of length $n>13$ exists, then either n $=$ 3 979 201 339 721749 133 016 171 583 224 100, or $n > 4\cdot 10^{33}$ . This improves the lower bound on the length of a long Barker sequence by a factor of nearly $2000$ . We also obtain eighteen additional integers $n<10^{50}$ that cannot be ruled out as the length of a Barker sequence, and find more than 237 000 additional candidates $n<10^{100}$ . These results are obtained by completing extensive searches for Wieferich prime pairs and using them, together with a number of arithmetic restrictions on $n$ , to construct qualifying integers below a given bound. We also report on some updated computations regarding open cases of the circulant Hadamard matrix problem.

This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.

In this work, we investigate a quaternion-based formulation of 3D Simultaneous Localization and Mapping with Extended Kalman Filter (EKF-SLAM) using relative pose measurements. We introduce a discrete-time derivation that avoids the normalization problem that often arises when using unit quaternions in Kalman filter and we study its observability properties. The consistency of the estimation errors with the corresponding covariance matrices is also evaluated. The approach is further tested on real data from the Rawseeds dataset and it is applied within a delayed-state EKF architecture for estimating a dense 3D map of an unknown environment. The contribution is motivated by the possibility of abstracting multi-sensorial information in terms of relative pose measurements and for its straightforward extensions to the multi robot case.

In this paper, an iterative learning control algorithm is adopted to solve the high-precision trajectory tracking issue of a wheeled mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties. The designed iterative learning control law adopts predictive, current and past learning items to drive the state variables, and input variables, and outputs variables converge to the bounded scope of their desired values. The algorithm can enhance the control performance, stability and robust characteristics. The rigorous mathematical proof of the convergence character of the proposed iterative learning control algorithm is given. The feasibility, effectiveness, and robustness of the proposed algorithm are illustrated by quantitative experiments and comparative analysis. The experimental results show that the proposed iterative learning control algorithm has an outstanding control effect on the trajectory tracking issue of wheeled mobile robots.

This paper presents a method of planning a sub-optimal trajectory for a mobile manipulator subject to mechanical and control constraints. The path of the end-effector is defined as a curve that can be parameterised by any scaling parameter—the reference trajectory of a mobile platform is not needed. Constraints connected with the existence of mechanical limits for a given manipulator configuration, collision avoidance conditions and control constraints are considered. Nonholonomic constraints in a Pfaffian form are explicitly incorporated to the control algorithm. To avoid manipulator singularities, the motion of the robot is planned in order to maximise the manipulability measure.

Reuse has long been a major goal of the knowledge engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis, we chose a well-known benchmark knowledge base (KB) system written in CLIPS, which was based on the propose and revise problem-solving method and which had a lift/elevator KB. The KB contained four components, including constraints and data tables, expressed in an ontology that reflects the propose and revise task structure. Sufficient trial data was extracted manually to demonstrate the approach on two alternative problem solvers: a spreadsheet (Excel) and a constraint logic solver (ECLiPSe). The next phase was to implement ExtrAKTor, which automated the process for the whole KB. Each KB that is processed results in a working system that is able to solve the corresponding configuration task (and not only for elevators). This is in contrast to earlier work, which produced abstract formulations of the problem-solving methods but which were unable to perform reuse of actual KBs. We subsequently used the ECLiPSe solver on some more demanding vertical transport configuration tasks. We found that we had to use a little-known propagation technique described by Le Provost and Wallace (1991). Further, our techniques did not use any heuristic “fix”’ information, yet we successfully dealt with a “thrashing” problem that had been a key issue in the original vertical transit work. Consequently, we believe we have developed a widely usable approach for solving this class of parametric design problem, by applying novel constraint-based problem solvers to data and formulae stored in existing KBs.

We combine a new data model, where the random classification is subjected to rather weakrestrictions which in turn are based on the Mammen−Tsybakov [E. Mammen and A.B. Tsybakov,Ann. Statis. 27 (1999) 1808–1829; A.B. Tsybakov,Ann. Statis. 32 (2004) 135–166.] small margin conditions,and the statistical query (SQ) model due to Kearns [M.J. Kearns, J. ACM45 (1998) 983–1006] to what we refer to as PAC + SQ model. We generalize the classconditional constant noise (CCCN) model introduced by Decatur [S.E. Decatur, inICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. MorganKaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to the noise modelorthogonal to a set of query functions. We show that every polynomial time PAC + SQ learning algorithm can beefficiently simulated provided that the random noise rate is orthogonal to the queryfunctions used by the algorithm given the target concept. Furthermore, we extend theconstant-partition classification noise (CPCN) model due to Decatur [S.E. Decatur, inICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. MorganKaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to what we call theconstant-partition piecewise orthogonal (CPPO) noise model. We show how statisticalqueries can be simulated in the CPPO scenario, given the partition is known to thelearner. We show how to practically use PAC +SQ simulators in the noise model orthogonal to the query space bypresenting two examples from bioinformatics and software engineering. This way, wedemonstrate that our new noise model is realistic.