Around the Fed : Stock market investing is a family affair

Related links: http://www.richmondfed.org/publications/research/region_focus/2010/q1/around_the_fed_weblinks.cfmEconomics

Jargon alert: Prisoner's dliemma

Related link(s): http://www.richmondfed.org/publications/research/region_focus/2009/fall/jargon_alert_weblinks.cfmEconomics

Around the Fed: Lending standards and the foreclosure crisis

Related link(s): http://www.richmondfed.org/publications/research/region_focus/2009/fall/around_the_fed_weblinks.cfmEconomics

Research spotlight : A tale of two Fed banks

Related links: http://www.richmondfed.org/publications/research/region_focus/2010/q1/policy_update_weblinks.cfmBanks and banking ; Monetary policy

Monotone Pieces Analysis for Qualitative Modeling

It is a crucial task to build qualitative models of industrial applications for model-based diagnosis. A Model Abstraction procedure is designed to automatically transform a quantitative model into qualitative model. If the data is monotone, the behavior can be easily abstracted using the corners of the bounding rectangle. Hence, many existing model abstraction approaches rely on monotonicity. But it is not a trivial problem to robustly detect monotone pieces from scattered data obtained by numerical simulation or experiments. This paper introduces an approach based on scale-dependent monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. simulation results, can be partitioned into quasi-monotone segments. The end points for the monotone segments are used as the initial set of landmarks for qualitative model abstraction. The qualitative model abstraction works as an iteratively refining process starting from the initial landmarks. The monotonicity analysis presented here can be used in constructing many other kinds of qualitative models; it is robust and computationally efficient

Quasi-monotonic segmentation of state variable behavior for reactive control

Real-world agents must react to changing conditions as they execute planned tasks. Conditions are typically monitored through time series representing state variables. While some predicates on these times series only consider one measure at a time, other predicates, sometimes called episodic predicates, consider sets of measures. We consider a special class of episodic predicates based on segmentation of the the measures into quasi-monotonic intervals where each interval is either quasi-increasing, quasi-decreasing, or quasi-flat. While being scale-based, this approach is also computational efficient and results can be computed exactly without need for approximation algorithms. Our approach is compared to linear spline and regression analysis

Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments

Qualitative models are often more suitable than classical quantitative models in tasks such as Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotonic pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper presents scale-based monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. sensor data or simulation results, can be partitioned into quasi-monotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm is introduced along with a scale-based definition of "flatness"

Democracy in 2022: Trump’s Rhetoric, Truth Social and Election Integrity Platforms

This thesis focuses on how Populism and Democracy remain constantly opposing forces in 21st century United States domestic politics. A focal point of populist sentiment, rhetoric and narrative dissemination is the Trumpian MAGA wing of the American right. This thesis seeks to address scholarly analysis of Donald Trump’s populist MAGA movement in the context of the 2022 Midterm election, highlight populist narratives of the movement through Truth Social posts and the negative effects upon voters in states with rhetorical focus from MAGA election integrity narratives. This thesis was done through a 3 month phenomenological study with the object of research being the phenomena of Trumpian narratives collected and observed on the former president’s social media platform, Truth Social. In Chapter 1, populist sentiments and traits are explored through scholarly literature and commentary. Chapter 2 proceeds to explore populism in action by reviewing and describing the former president’s new social media. Chapter 3 takes narratives driven by Trump on Truth Social, such as election integrity narratives in 5 key battle ground states, to explore the threat of populist rhetoric to democracy. All of this is done with the underlying danger and threat of populist language and rhetoric to bring harm to institutions in the United States. The conclusion being that MAGA conservative rhetoric may propel political and social disruption within the United States through lenses of fear among and toward the Trumpian base. This turbulence is expected to have damaging effects on voters access to and trust of the electoral system of the United States

An analysis and visualization of the output mode-matching requirements for squeezing in Advanced LIGO and future gravitational wave detectors

The sensitivity of ground-based gravitational wave (GW) detectors will be improved in the future via the injection of frequency-dependent squeezed vacuum. The achievable improvement is ultimately limited by losses of the interferometer electromagnetic field that carries the GW signal. The analysis and reduction of optical loss in the GW signal chain will be critical for optimal squeezed light-enhanced interferometry. In this work we analyze a strategy for reducing output-side losses due to spatial mode mismatch between optical cavities with the use of adaptive optics. Our goal is not to design a detector from the top down, but rather to minimize losses within the current design. Accordingly, we consider actuation on optics already present and one transmissive optic to be added between the signal recycling mirror and the output mode cleaner. The results of our calculation show that adaptive mode-matching with the current Advanced LIGO design is a suitable strategy for loss reduction that provides less than 2% mean output mode-matching loss. The range of actuation required is +47 uD on SR3, +140 mD on OM1 and OM2, +50 mD on the SRM substrate, and -50 mD on the added new transmissive optic. These requirements are within the demonstrated ranges of real actuators in similar or identical configurations to the proposed implementation. We also present a novel technique that graphically illustrates the matching of interferometer modes and allows for a quantitative comparison of different combinations of actuators.Comment: Matches version accepted in PR