Particle parameter analyzing system

An X-Y plotter circuit apparatus is described which displays an input pulse representing particle parameter information, that would ordinarily appear on the screen of an oscilloscope as a rectangular pulse, as a single dot positioned on the screen where the upper right hand corner of the input pulse would have appeared. If another event occurs, and it is desired to display this event, the apparatus is provided to replace the dot with a short horizontal line

Analyzing Inventory Investment

macroeconomics, inventory investment

Analyzing female labor supply: Evidence from a Dutch tax reform

This paper uses the exogenous variation caused by the Dutch tax reform of 2001 to investigate how married women react to financial incentives. Among OECD countries, the Netherlands has average female labor force participation, but by far the highest rate of part-time work. Our main conclusion is that the positive significant effect of the 2001 tax reform on labor force participation dominates the negative insignificant effect on working hours. Our preferred explanation is that women respond more to changes in tax allowances than to changes in marginal tax rates.

Analyzing Quantitative Models

How can a potential user distinguish between a quantitative model that may be of some real value and one that is not? The model builder rarely provides much help, since most are advocates of their own work and tend to lose their objectivity toward the model. Therefore, an independent evaluation is necessary to judge the true usefulness of the model.quantitative models, analysis

Analyzing the Facebook Friendship Graph

Online Social Networks (OSN) during last years acquired a\ud huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re\ud ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities\ud between the developing of OSN and real-life social networks

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Analyzing rebound effects

Are efficiency improvements in the use of natural resources the key for sustainable development, are they the solution to environmental problems, or will second round effects - so-called rebound effects - compensate or even overcompensate potential savings, will they fire back? The answer to this question will have fundamental policy implications but the research on rebound effects does not provide clear results. This paper aims to clarify the theoretical basis of various analytical approaches which lead to widely different estimates of rebound effects. -- Sind Verbesserungen in der Effizienz im Umgang mit den natürlichen Ressourcen der Schlüssel für eine Nachhaltige Entwicklung, können sie eine Lösung für die Umweltprobleme sein? Oder werden die potenziellen Einsparungen durch so genannte Rebound-Effekte wieder aufgefressen, also kompensiert oder gar überkompensiert? Die Antwort hierauf hat weitreichende politische Implikationen, doch die Forschung zu Rebound-Effekten liefert keine klaren Ergebnisse. Dieses Papier soll dazu beitragen, die theoretische Basis verschiedener analytischer Zugänge, die zu weit voneinander abweichenden Abschätzungen der Rebound-Effekte kommen, zu klären.

Analyzing and clustering neural data

This thesis aims to analyze neural data in an overall effort by the Charles Stark Draper Laboratory to determine an underlying pattern in brain activity in healthy individuals versus patients with a brain degenerative disorder. The neural data comes from ECoG (electrocorticography) applied to either humans or primates. Each ECoG array has electrodes that measure voltage variations which neuroscientists claim correlates to neurons transmitting signals to one another. ECoG differs from the less invasive technique of EEG (electroencephalography) in that EEG electrodes are placed above a patients scalp while ECoG involves drilling small holes in the skull to allow electrodes to be closer to the brain. Because of this ECoG boasts an exceptionally high signal-to-noise ratio and less susceptibility to artifacts than EEG [6]. While wearing the ECoG caps, the patients are asked to perform a range of different tasks. The tasks performed by patients are partitioned into different levels of mental stress i.e. how much concentration is presumably required. The specific dataset used in this thesis is derived from cognitive behavior experiments performed on primates at MGH (Massachusetts General Hospital). The content of this thesis can be thought of as a pipelined process. First the data is collected from the ECoG electrodes, then the data is pre-processed via signal processing techniques and finally the data is clustered via unsupervised learning techniques. For both the pre-processing and the clustering steps, different techniques are applied and then compared against one another. The focus of this thesis is to evaluate clustering techniques when applied to neural data. For the pre-processing step, two types of bandpass filters, a Butterworth Filter and a Chebyshev Filter were applied. For the clustering step three techniques were applied to the data, K-means Clustering, Spectral Clustering and Self-Tuning Spectral Clustering. We conclude that for pre-processing the results from both filters are very similar and thus either filter is sufficient. For clustering we conclude that K- means has the lowest amount of overlap between clusters. K-means is also the most time-efficient of the three techniques and is thus the ideal choice for this application.2016-10-27T00:00:00