Integrated Inertial/gps

The presence of failures in navigation sensors can cause the determination of an erroneous aircraft state estimate, which includes position, attitude, and their derivatives. Aircraft flight control systems rely on sensor inputs to determine the aircraft state. In the case of integrated Inertial/NAVSTAR Global Positioning System (GPS), sensor failures could occur in the on-board inertial sensors or in the GPS measurements. The synergistic use of both GPS and the Inertial Navigation System (INS) allows for highly reliable fault detection and isolation of sensor failures. Integrated Inertial/GPS is a promising technology for the High Speed Civil Transport (HSCT) and the return and landing of a manned space vehicle

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge

HERA Diffractive Structure Function Data and Parton Distributions

Recent diffractive structure function measurements by the H1 and ZEUS experiments at HERA are reviewed. Various data sets, obtained using systematically different selection and reconstruction methods, are compared. NLO DGLAP QCD fits are performed to the most precise H1 and ZEUS data and diffractive parton densities are obtained in each case. Differences between the Q^2 dependences of the H1 and ZEUS data are reflected as differences between the diffractive gluon densities.Comment: Contributed to the Proceedings of the Workshop on HERA and the LHC, DESY and CERN, 2004-200

Identification of Causal Effects on Binary Outcomes Using Structural Mean Models

Structural mean models (SMMs) are used to estimate causal effects among those selecting treatment in randomised controlled trials affected by non-ignorable non-compliance. These causal effects can be identified by assuming that there is no effect modification, namely, that the causal effect is equal for the treated subgroups randomised to treatment and to control. By analysing simple structural models for binary outcomes, we argue that the no effect modification assumption does not hold in general, and so SMMs do not estimate causal effects for the treated. An exception is for designs in which those randomised to control can be completely excluded from receiving the treatment. However, when there is non-compliance in the control arm, local (or complier) causal effects can be identified provided that the further assumption of monotonic selection into treatment holds. We demonstrate these issues using numerical examples.structural mean models, identification, local average treatment effects, complier average treatment effects

Do social preferences matter in competitive markets?

Experimental evidence stresses the importance of so–called social preferences for understanding economic behavior. Social preferences are defined over the entire allocation in a given economic environment, and not just over one’s own consumption as is traditionally presumed. We study the implications for competitive market outcomes if agents have such preferences. First, we clarify under what conditions an agent behaves as if she was selfish—i.e. when her demand function is independent of others’ behavior. An agent behaves as if selfish if and only if her preferences can be represented by a utility function that is separable between her own utility and the allocation of goods for all other agents. Next, we study equilibrium outcomes in economies where individual agents behave as if selfish. We show that one can identify a corresponding ego–economy such that the equilibria of the ego–economy coincide with the equilibria of the original economy. As a consequence, competitive equilibria exist and they are material efficient. In general, however, the First Welfare Theorem fails. We introduce the class of Bergsonian social utility functions, which are social utility functions that are completely separable in all agents’ material utility. For such social preferences, the Second Welfare Theorem holds under a suitable growth condition. We also establish that in uncertain environments, agents with social preferences typically do not behave as if selfish. Furthermore, in the presence of public goods, both demand and equilibrium outcomes depend on social preferences.

Instrumental Variable Estimators for Binary Outcomes

The estimation of exposure effects on study outcomes is almost always complicated by non-random exposure selection - even randomised controlled trials can be affected by participant non-compliance. If the selection mechanism is non-ignorable then inferences based on estimators that fail to adjust for its effects will be misleading. Potentially consistent estimators of the exposure effect can be obtained if the data are expanded to include one or more instrumental variables (IVs). An IV must satisfy core conditions constraining it to be associated with the exposure, and indirectly (but not directly) associated with the outcome through this association. Here we consider IV estimators for studies in which the outcome is represented by a binary variable. While work on this problem has been carried out in statistics and econometrics, the estimators and their associated identifying assumptions have existed in the separate domains of structural models and potential outcomes with almost no overlap. In this paper, we review and integrate the work in these areas and reassess the issues of parameter identification and estimator consistency. Identification of maximum likelihood estimators comes from strong parametric modelling assumptions, with consistency depending on these assumptions being correct. Our main focus is on three semi-parametric estimators based on the generalised method of moments, marginal structural models and structural mean models (SMM). By inspecting the identifying assumptions for each method, we show that these estimators are inconsistent even if the true model generating the data is simple, and argue that this implies that consistency is obtained only under implausible conditions. Identification for SMMs can also be obtained under strong exposure-restricting design constraints that are often appropriate for randomised controlled trials, but not for observational studies. Finally, while estimation of local causal parameters is possible if the selection mechanism is monotonic, not all SMMs identify a local parameter.Econometrics, Generalized methods of moments, Parameter identification, Marginal structural models, Structural mean models, Structural models

Instrumental Variable Estimators for Binary Outcomes

Instrumental variables (IVs) can be used to construct estimators of exposure effects on the outcomes of studies affected by non-ignorable selection of the exposure. Estimators which fail to adjust for the effects of non-ignorable selection will be biased and inconsistent. Such situations commonly arise in observational studies, but even randomised controlled trials can be affected by non-ignorable participant non-compliance. In this paper, we review IV estimators for studies in which the outcome is binary. Recent work on identification is interpreted using an integrated structural modelling and potential outcomes framework, within which we consider the links between different approaches developed in statistics and econometrics. The implicit assumptions required for bounding causal effects and point-identification by each estimator are highlighted and compared within our framework. Finally, the implications for practice are discussed.bounds, causal inference, generalized method of moments, local average treatment effects, marginal structural models, non-compliance, parameter identification, potential outcomes, structural mean models, structural models

The Impact of Organic Cotton Farming on the Livelihoods of Smallholders. Evidence from the Maikaal bioRe poject in central India

This research report analyses the impact of conversion to organic cotton farming on the livelihoods of smallholders in the Maikaal bioRe organic cotton project in Madhya Pradesh, central India. For that purpose, it compares farm profile data, material and financial input/output and soil parameters of organic and conventional farms over two cropping periods (2003 – 2005). The results show that organic farms achieve cotton yields that are on a par with those in conventional farms, though nutrient inputs are considerably lower. With less production costs and a 20% organic price premium, gross margins from cotton are thus substantially higher than in the conventional system. Even if the crops grown in rotation with cotton are sold without organic price premium, profits in organic farms are higher. In the perception of most organic farmers, soil fertility significantly improved after conversion. However, the analysis of soil fertility parameters in soil samples from organic and conventional cotton fields has shown only minor differences in organic matter content and water retention. The research indicates that organic cotton farming can be a viable option to improve incomes and reduce vulnerability of smallholders in the tropics. To use this potential it is important to find suitable approaches to enable marginalised farmers managing the hurdles of conversion to the organic farming system

Finite-sample instrumental variables inference using an asymptotically pivotal statistic

This paper is concerned with investigating the role of accounting practices in radical change processes. The institutional framework has been taken as a starting point in investigating these processes. The research has been carried out at the Dutch Railways. This company was forced by the Dutch government to change from a public company into a private company. This decision by the Dutch Government has had radical consequences for Dutch Railways’ position in the (rail) transport market and for the way of managing the company. The research focuses on the processes in which the company has changed its template as a public company into a profit-oriented template. This paper examines the interaction of accounting practices with the environmental and organisational context. Emphasis is placed on how these mutual processes of interaction change internal and external positioning, create new visibilities, transform perspectives on organisational activities and performance and modify conditions for organisational change. Existing institutional concepts regarding change processes are evaluated in the light of the case findings and building blocks are developed for a comprehensive change framework.