We Are Not Machines

CCC_we_are_not_machines.pdf: 1551 downloads, before Oct. 1, 2020

Measuring Well-Being: A Review of Instruments

Interest in the study of psychological health and well-being has increased significantly in recent decades. A variety of conceptualizations of psychological health have been proposed including hedonic and eudaimonic well-being, quality-of-life, and wellness approaches. Although instruments for measuring constructs associated with each of these approaches have been developed, there has been no comprehensive review of well-being measures. The present literature review was undertaken to identify self-report instruments measuring well-being or closely related constructs (i.e., quality of life and wellness) and critically evaluate them with regard to their conceptual basis and psychometric properties. Through a literature search, we identified 42 instruments that varied significantly in length, psychometric properties, and their conceptualization and operationalization of well-being. Results suggest that there is considerable disagreement regarding how to properly understand and measure well-being. Research and clinical implications are discussed

An Efficient Global Optimality Certificate for Landmark-Based SLAM

Modern state estimation is often formulated as an optimization problem and solved using efficient local search methods. These methods at best guarantee convergence to local minima, but, in some cases, global optimality can also be certified. Although such global optimality certificates have been well established for 3D \textit{pose-graph optimization}, the details have yet to be worked out for the 3D landmark-based SLAM problem, in which estimated states include both robot poses and map landmarks. In this paper, we address this gap by using graph-theoretic approach to cast the subproblems of landmark-based SLAM into a form that yields a sufficient condition for global optimality. Efficient methods of computing the optimality certificates for these subproblems exist, but first require the construction of a large data matrix. We show that this matrix can be constructed with complexity that remains linear in the number of landmarks and does not exceed the state-of-the-art computational complexity of one local solver iteration. We demonstrate the efficacy of the certificate on simulated and real-world landmark-based SLAM problems. Finally, we study the robustness of the global optimality certificate to measurement noise, taking into consideration the effect of the underlying measurement graph.Comment: 8 pages, 7 figure

Safe and Smooth: Certified Continuous-Time Range-Only Localization

A common approach to localize a mobile robot is by measuring distances to points of known positions, called anchors. Locating a device from distance measurements is typically posed as a non-convex optimization problem, stemming from the nonlinearity of the measurement model. Non-convex optimization problems may yield suboptimal solutions when local iterative solvers such as Gauss-Newton are employed. In this paper, we design an optimality certificate for continuous-time range-only localization. Our formulation allows for the integration of a motion prior, which ensures smoothness of the solution and is crucial for localizing from only a few distance measurements. The proposed certificate comes at little additional cost since it has the same complexity as the sparse local solver itself: linear in the number of positions. We show, both in simulation and on real-world datasets, that the efficient local solver often finds the globally optimal solution (confirmed by our certificate), but it may converge to local solutions with high errors, which our certificate correctly detects.Comment: 10 pages, 7 figures, accepted to IEEE Robotics and Automation Letters (this arXiv version contains supplementary appendix

On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics

In recent years, there has been remarkable progress in the development of so-called certifiable perception methods, which leverage semidefinite, convex relaxations to find global optima of perception problems in robotics. However, many of these relaxations rely on simplifying assumptions that facilitate the problem formulation, such as an isotropic measurement noise distribution. In this paper, we explore the tightness of the semidefinite relaxations of matrix-weighted (anisotropic) state-estimation problems and reveal the limitations lurking therein: matrix-weighted factors can cause convex relaxations to lose tightness. In particular, we show that the semidefinite relaxations of localization problems with matrix weights may be tight only for low noise levels. We empirically explore the factors that contribute to this loss of tightness and demonstrate that redundant constraints can be used to regain tightness, albeit at the expense of real-time performance. As a second technical contribution of this paper, we show that the state-of-the-art relaxation of scalar-weighted SLAM cannot be used when matrix weights are considered. We provide an alternate formulation and show that its SDP relaxation is not tight (even for very low noise levels) unless specific redundant constraints are used. We demonstrate the tightness of our formulations on both simulated and real-world data

CONCENTRATION ISSUES IN THE U.S. BEEF SUBSECTOR

Industrial Organization, Livestock Production/Industries,

Free movement of goods, persons, services and capital within the European Union. Jurisprudential Adjudications by the Court of Justice.

The published work which forms the basis of this submission by the applicant for the Degree of Doctor of Philosophy represents an extensive research which has extended the boundaries of knowledge and understanding in relation to the jurisprudential adjudications by the Court of Justice concerning the application of the TFEU freedoms of goods, persons, services and capital to national measures. The publications maintain thematic analytical focus on the jurisprudential employment of the EU principles of non discrimination, market access and the rule relating to the ¿selling arrangement¿ that are used as the modus operandi in the acquisition of Treaty free movement rights. The thread is law making; the published work evidences inconsistencies, complexities and confusions in the application by the Court of Justice of the modus operandi used to ensure acquisition of Treaty free movement rights. The research depicts a goods, persons, services and capital jurisprudence which displays a want of thematically consistent underpinning and some doctrinal diversity. It is the purpose of this Submission to exhibit the cohesiveness of the published work under review in the context of the contribution made to the knowledge and understanding of the jurisprudence of goods, persons, services and capital in European Union law

Toward Globally Optimal State Estimation Using Automatically Tightened Semidefinite Relaxations

In recent years, semidefinite relaxations of common optimization problems in robotics have attracted growing attention due to their ability to provide globally optimal solutions. In many cases, it was shown that specific handcrafted redundant constraints are required to obtain tight relaxations and thus global optimality. These constraints are formulation-dependent and typically require a lengthy manual process to find. Instead, the present paper suggests an automatic method to find a set of sufficient redundant constraints to obtain tightness, if they exist. We first propose an efficient feasibility check to determine if a given set of variables can lead to a tight formulation. Secondly, we show how to scale the method to problems of bigger size. At no point of the process do we have to manually find redundant constraints. We showcase the effectiveness of the approach, in simulation and on real datasets, for range-based localization and stereo-based pose estimation. Finally, we reproduce semidefinite relaxations presented in recent literature and show that our automatic method finds a smaller set of constraints sufficient for tightness than previously considered.Comment: 18 pages, 20 figure

Most Common Statistical Methodologies in Recent Clinical Studies of Community-Acquired Pneumonia

Background: Training new individuals in pneumonia research is imperative to produce a new generation of clinical investigators with the expertise necessary to fill gaps in knowledge. Clinical investigators are often intimidated by their unfamiliarity with statistics. The objective of this study is to define the most common statistical methodologies in recent clinical studies of CAP to inform teaching approaches in the field. Methods: Articles met inclusion criteria if they were clinical research with an emphasis on incidence, epidemiology, or patient outcomes, searchable via PubMed or Google Scholar, published within the timeframe of January 1st 2012 to August 1st 2017, and contained Medical Subject Headings (MeSH) keywords of “pneumonia” and one of the following: “epidemiologic studies”, “health services research”, or “comparative effectiveness research” or search keywords of community-acquired pneumonia” and one of the following: “cohort study”, “observational study”, “prospective study”, “retrospective study”, “clinical trial”, “controlled trial”, or “clinical study”. Descriptive statistics for the most common statistical methods were reported. Results: Thirty articles were included in the analysis. Descriptive statistics most commonly contained within articles were frequency (n=30 [100%]) and percent (n=30 [100%]), along with medians (n=22 [73%]) and interquartile ranges (n=19 [63%]). Most commonly performed analytical statistics were the Chi-squared test (n=20 [67%]), logistic regression (n=18 [60%]), Fisher’s exact test (n=17 [57%]), Wilcoxon rank sum test (n=16 [53%]), T-test (n=13 [43%]), and Cox proportional hazards regression (n=10 [33%]). Conclusions: We identified the most common clinical research tests performed in studies of hospitalized patients with CAP. Junior investigators should become very familiar with these tests early in their research careers