Correcting the Hub Occurrence Prediction Bias in Many Dimensions

Data reduction is a common pre-processing step for k-nearest neighbor classification (kNN). The existing prototype selection methods implement different criteria for selecting relevant points to use in classification, which constitutes a selection bias. This study examines the nature of the instance selection bias in intrinsically high-dimensional data. In high-dimensional feature spaces, hubs are known to emerge as centers of influence in kNN classification. These points dominate most kNN sets and are often detrimental to classification performance. Our experiments reveal that different instance selection strategies bias the predictions of the behavior of hub-points in high-dimensional data in different ways. We propose to introduce an intermediate un-biasing step when training the neighbor occurrence models and we demonstrate promising improvements in various hubness-aware classification methods, on a wide selection of high-dimensional synthetic and real-world datasets

How people talk about each other: Modeling Generalized Intergroup Bias and Emotion

Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigating negative bias, and having a clear notion of the targeted group is necessary, it is not always practical. In this work we extrapolate to a broader notion of bias, rooted in social science and psychology literature. We move towards predicting interpersonal group relationship (IGR) - modeling the relationship between the speaker and the target in an utterance - using fine-grained interpersonal emotions as an anchor. We build and release a dataset of English tweets by US Congress members annotated for interpersonal emotion -- the first of its kind, and 'found supervision' for IGR labels; our analyses show that subtle emotional signals are indicative of different biases. While humans can perform better than chance at identifying IGR given an utterance, we show that neural models perform much better; furthermore, a shared encoding between IGR and interpersonal perceived emotion enabled performance gains in both tasks. Data and code for this paper are available at https://github.com/venkatasg/interpersonal-biasComment: To be presented at EACL 202

The applications of satellites to communications, navigation and surveillance for aircraft operating over the contiguous United States. Volume 1 - Technical report

Satellite applications to aircraft communications, navigation, and surveillance over US including synthesized satellite network and aircraft equipment for air traffic contro

Co-design of sectoral climate services based on seasonal prediction information in the Mediterranean

We present in this contribution the varied experiences gathered in the co-design of a sectoral climate services collection, developed in the framework of the MEDSCOPE project, which have in common the application of seasonal predictions for the Mediterranean geographical and climatic region. Although the region is affected by low seasonal predictability limiting the skill of seasonal forecasting systems, which historically has hindered the development of downstream services, the project was originally conceived to exploit windows of opportunity with enhanced skill for developing and evaluating climate services in various sectors with high societal impact in the region: renewable energy, hydrology, and agriculture and forestry. The project also served as the scientific branch of the WMO-led Mediterranean Climate Outlook Forum (MedCOF) that had as objective -among others- partnership strengthening on climate services between providers and users within the Mediterranean region. The diversity of the MEDSCOPE experiences in co-designing shows the wide range of involvement and engagement of users in this process across the Mediterranean region, which benefits from the existing solid and organized MedCOF community of climate services providers and users. A common issue among the services described here -and also among other prototypes developed in the project- was related with the communication of forecasts uncertainty and skill for efficiently informing decision-making in practice. All MEDSCOPE project prototypes make use of an internally developed software package containing process-based methods for synthesising seasonal forecast data, as well as basic and advanced tools for obtaining tailored products. Another challenge assumed by the project refers to the demonstration of the economic, social, and environmental value of predictions provided by these MEDSCOPE prototypes.The work described in this paper has received funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462.Peer Reviewed"Article signat per 16 autors/es: Eroteida Sánchez-García, Ernesto Rodríguez-Camino, Valentina Bacciu, Marta Chiarle, José Costa-Saura, Maria Nieves Garrido, Llorenç Lledó, Beatriz Navascués, Roberta Paranunzio, Silvia Terzag, Giulio Bongiovanni, Valentina Mereu, Guido Nigrelli, Monia Santini, Albert Soret, Jostvon Hardenberg"Postprint (published version

Co-design of sectoral climate services based on seasonal prediction information in the Mediterranean

We present in this contribution the varied experiences gathered in the co-design of a sectoral climate services collection, developed in the framework of the MEDSCOPE project, which have in common the application of seasonal predictions for the Mediterranean geographical and climatic region. Although the region is affected by low seasonal predictability limiting the skill of seasonal forecasting systems, which historically has hindered the development of downstream services, the project was originally conceived to exploit windows of opportunity with enhanced skill for developing and evaluating climate services in various sectors with high societal impact in the region: renewable energy, hydrology, and agriculture and forestry. The project also served as the scientific branch of the WMO-led Mediterranean Climate Outlook Forum (MedCOF) that had as objective -among others- partnership strengthening on climate services between providers and users within the Mediterranean region. The diversity of the MEDSCOPE experiences in co-designing shows the wide range of involvement and engagement of users in this process across the Mediterranean region, which benefits from the existing solid and organized MedCOF community of climate services providers and users. A common issue among the services described here -and also among other prototypes developed in the project- was related with the communication of forecasts uncertainty and skill for efficiently informing decision-making in practice. All MEDSCOPE project prototypes make use of an internally developed software package containing process-based methods for synthesising seasonal forecast data, as well as basic and advanced tools for obtaining tailored products. Another challenge assumed by the project refers to the demonstration of the economic, social, and environmental value of predictions provided by these MEDSCOPE prototypes.The work described in this paper has received funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462

Cosmology Using Cluster Internal Velocity Dispersions

We compare the distribution of internal velocity dispersions of galaxy clusters for an observational sample to those obtained from a set of N-body simulations of seven COBE-normalised cosmological scenarios: the standard CDM (SCDM) and a tilted (n=0.85) CDM (TCDM) model, a CHDM model with 25% of massive neutrinos, two low-density LCDM models with Omega_0=0.3 and 0.5, two open OCDM models with Omega_0=0.4 and 0.6. Simulated clusters are observed in projection so as to reproduce the main observational biases and are analysed by applying the same algorithm for interlopers removal and velocity dispersion estimate as for the reference observational sample. Velocity dispersions for individual clusters can be largely affected by observational biases in a model-dependent way: models in which clusters had less time to virialize show larger discrepancies between 3D and projected velocity dispersions. From the comparison with real clusters we find that both SCDM and TCDM largely overproduce clusters. The CHDM model marginally overproduces clusters and requires a somewhat larger sigma_8 than a purely CDM model in order to produce the same cluster abundance. The LCDM model with Omega_0=0.3 agrees with data, while the open model with Omega_0=0.4 and 0.6 underproduces and marginally overproduces clusters, respectively.Comment: 28 pages, LaTeX uses Elsevier style file, 7 postscript figures (3 bitmapped to lower res.) included. Submitted to New Astronom