560 research outputs found
Does money matter in inflation forecasting?.
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation
Sensitive gravity-gradiometry with atom interferometry: progress towards an improved determination of the gravitational constant
We here present a high sensitivity gravity-gradiometer based on atom
interferometry. In our apparatus, two clouds of laser-cooled rubidium atoms are
launched in fountain configuration and interrogated by a Raman interferometry
sequence to probe the gradient of gravity field. We recently implemented a
high-flux atomic source and a newly designed Raman lasers system in the
instrument set-up. We discuss the applications towards a precise determination
of the Newtonian gravitational constant G. The long-term stability of the
instrument and the signal-to-noise ratio demonstrated here open interesting
perspectives for pushing the measurement precision below the 100 ppm level
Bragg gravity-gradiometer using the S-P intercombination transition of Sr
We present a gradiometer based on matter-wave interference of
alkaline-earth-metal atoms, namely Sr. The coherent manipulation of the
atomic external degrees of freedom is obtained by large-momentum-transfer Bragg
diffraction, driven by laser fields detuned away from the narrow
S-P intercombination transition. We use a well-controlled
artificial gradient, realized by changing the relative frequencies of the Bragg
pulses during the interferometer sequence, in order to characterize the
sensitivity of the gradiometer. The sensitivity reaches
s for an interferometer time of 20 ms, limited only by geometrical
constraints. We observed extremely low sensitivity of the gradiometric phase to
magnetic field gradients, approaching a value 10 times lower than the
sensitivity of alkali-atom based gradiometers. An efficient double-launch
technique employing accelerated red vertical lattices from a single
magneto-optical trap cloud is also demonstrated. These results highlight
strontium as an ideal candidate for precision measurements of gravity
gradients, with potential application in future precision tests of fundamental
physics.Comment: 10 pages, 7 figure
A multi-perspective analysis of social context and personal factors in office settings for the design of an effective mobile notification system
In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels of knowledge workers. We also developed multi-level (within- and between-subjects) models for the inference of attentional states and engagement/challenge levels with mobile application usage indicators as inputs, such as the number of applications used prior to notifications, the number of switches between applications, and application category usage. The results of our analysis show that the following features are effective for the inference of attentional states and engagement/challenge levels: the number of switches between mobile applications in the last 45 minutes and the duration of application usage in the last 5 minutes before users' response to ESM messages
Machine Learning and Data Analysis in Astroinformatics
Astroinformatics is a new discipline at the cross-road of astronomy, advanced statistics and computer science. With next generation sky surveys, space missions and modern instrumentation astronomy will enter the Petascale regime raising the demand for advanced computer science techniques with hard- and software solutions for data management, analysis, efficient automation and knowledge discovery. This tutorial reviews important developments in astroinformatics over the past years and discusses some relevant research questions and concrete problems. The contribution ends with a short review of the special session papers in these proceedings, as well as perspectives and challenges for the near future
Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets
Data augmentation is rapidly gaining attention in machine
learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case,
the main challenge is to estimate the label associated to new
synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the
use of these as unsupervised information in a semi-supervised
learning framework with support vector machines, avoiding
thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Our
results show that this type of data over-sampling supports
the well-known cluster assumption in semi-supervised learning, showing outstanding results for small high-dimensional
datasets and imbalanced learning problems
Evolution, recurrency and kernels in learning to model inflation
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both
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