Lazy training of radial basis neural networks

Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space. This makes non-local methods, like Neural Networks, not very accurate in those cases. On the other hand, local methods have the problem of how to know which are the best examples for each test pattern. In this work, we present a way of performing a trade off between local and non-local methods. On one hand a Radial Basis Neural Network is used like learning algorithm, on the other hand a selection of the training patterns is used for each query. Moreover, the RBNN initialization algorithm has been modified in a deterministic way to eliminate any initial condition influence. Finally, the new method has been validated in two time series domains, an artificial and a real world one.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-0

Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-

Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks). In particular, we show that a simple universal perturbation can fool a series of state-of-the-art defenses. Adversarial examples generated by existing attacks are generally hard to transfer to defense models. We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations. Therefore, we propose an effective transforming paradigm and a customized gradient transformer module to transform existing perturbations into regionally homogeneous ones. Without explicitly forcing the perturbations to be universal, we observe that a well-trained gradient transformer module tends to output input-independent gradients (hence universal) benefiting from the under-fitting phenomenon. Thorough experiments demonstrate that our work significantly outperforms the prior art attacking algorithms (either image-dependent or universal ones) by an average improvement of 14.0% when attacking 9 defenses in the black-box setting. In addition to the cross-model transferability, we also verify that regionally homogeneous perturbations can well transfer across different vision tasks (attacking with the semantic segmentation task and testing on the object detection task).Comment: The code is available here: https://github.com/LiYingwei/Regional-Homogeneit

Opioid Doses and Acute Care Utilization Outcomes for Adults with Sickle Cell Disease: Emergency Department versus Acute Care Unit

Background Acute care units (ACUs) with focused sickle cell disease (SCD) care have been shown to effectively address pain and limit hospitalizations compared to emergency departments (ED), the reason for differences in admission rates is understudied. Our aim was compare effects of usual care for adult SCD pain in ACU and ED on opioid doses and discharge pain ratings, hospital admission rates and lengths of stay. Methods In a retrospective, comparative cohort, single academic tertiary center study, 148 adults with sickle cell pain received care in the ED, ACU or both. From the medical records we documented opioid doses, unit discharge pain ratings, hospital admission rates, and lengths of stay. Findings Pain on admission to the ED averaged 8.7 ± 1.5 and to the ACU averaged 8.0 ± 1.6. The average pain on discharge from the ED was 6.4 ± 3.0 and for the ACU was 4.5 ± 2.5. 70% of the 144 ED visits resulted in hospital admissions as compared to 37% of the 73 ACU visits. Admissions from the ED or ACU had similar inpatient lengths of stay. Significant differences between ED and ACU in first opioid dose and hourly opioid dose were noted. Conclusions Applying guidelines for higher dosing of opioids for acute painful episodes in adults with SCD in ACU was associated with improved pain outcomes and decreased hospitalizations, compared to ED. Adoption of this approach for SCD pain in ED may result in improved outcomes, including a decrease in hospital admissions

Disrupted in Schizophrenia 1 Regulates Neuronal Progenitor Proliferation via Modulation of GSK3β/β-Catenin Signaling

The Disrupted in Schizophrenia 1 (DISC1) gene is disrupted by a balanced chromosomal translocation (1; 11) (q42; q14.3) in a Scottish family with a high incidence of major depression, schizophrenia, and bipolar disorder. Subsequent studies provided indications that DISC1 plays a role in brain development. Here, we demonstrate that suppression of DISC1 expression reduces neural progenitor proliferation, leading to premature cell cycle exit and differentiation. Several lines of evidence suggest that DISC1 mediates this function by regulating GSK3β. First, DISC1 inhibits GSK3β activity through direct physical interaction, which reduces β-catenin phosphorylation and stabilizes β-catenin. Importantly, expression of stabilized β-catenin overrides the impairment of progenitor proliferation caused by DISC1 loss of function. Furthermore, GSK3 inhibitors normalize progenitor proliferation and behavioral defects caused by DISC1 loss of function. Together, these results implicate DISC1 in GSK3β/β-catenin signaling pathways and provide a framework for understanding how alterations in this pathway may contribute to the etiology of psychiatric disorders.National Alliance for Research on Schizophrenia and Depression (U.S.) (Young Investigator Award)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Award)Human Frontier Science Program (Strasbourg, France) (Fellowship)Singleton FellowshipNational Institutes of Health (U.S.) (Grant NS37007

Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantavirus, with characteristics of fever, hemorrhage, kidney damage, and hypotension. HFRS is recognized as a notifiable public health problem in China, and Liaoning Province is one of the most seriously affected areas with the most cases in China. It is necessary to investigate the spatial, temporal, and space-time distribution of confirmed cases of HFRS in Liaoning Province, China for future research into risk factors.</p> <p>Methods</p> <p>A cartogram map was constructed; spatial autocorrelation analysis and spatial, temporal, and space-time cluster analysis were conducted in Liaoning Province, China over the period 1988-2001.</p> <p>Results</p> <p>When the number of permutation test was set to 999, Moran's I was 0.3854, and was significant at significance level of 0.001. Spatial cluster analysis identified one most likely cluster and four secondary likely clusters. Temporal cluster analysis identified 1998-2001 as the most likely cluster. Space-time cluster analysis identified one most likely cluster and two secondary likely clusters.</p> <p>Conclusions</p> <p>Spatial, temporal, and space-time scan statistics may be useful in supervising the occurrence of HFRS in Liaoning Province, China. The result of this study can not only assist health departments to develop a better prevention strategy but also potentially increase the public health intervention's effectiveness.</p

Research on an online self-organizing radial basis function neural network

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms