91,404 research outputs found
The correlation structure of spatial autoregressions
This paper investigates how the correlations implied by a first-order simultaneous
autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation
parameter. A graph theoretic representation of the covariances in terms of
walks connecting the spatial units helps to clarify a number of correlation properties
of the processes. In particular, we study some implications of row-standardizing
the weights matrix, the dependence of the correlations on graph distance, and the
behavior of the correlations at the extremes of the parameter space. Throughout
the analysis differences between directed and undirected networks are emphasized.
The graph theoretic representation also clarifies why it is difficult to relate properties
ofW to correlation properties of SAR(1) models defined on irregular lattices
Gray’s revised Reinforcement Sensitivity Theory in relation to Attention-Deficit/Hyperactivity and Tourette-like behaviors in the general population
Attention-Deficit/Hyperactivity Disorder (ADHD) and Tourette Syndrome (TS) present as distinct conditions clinically; however, they show comorbidity and inhibitory control deficits have been proposed to underlie both. The role of reinforcement sensitivity in ADHD has been studied previously, but no study has addressed this in relation to TS-like behaviors in the general population. The present study examined these associations within the remit of the revised Reinforcement Sensitivity Theory (rRST). One hundred and thirty-eight participants completed psychometric measures of the rRST, and self-report checklists for ADHD- and TS-like behaviors
Handedness and behavioural inhibition:left-handed females show most inhibition as measured by BIS/BAS self-report
This study investigated the relationship between handedness, gender and behavioural approach and inhibition using Carver and White’s (1994) BIS/BAS Scale. 112 participants took part: 46 left-handers and 66 right-handers. All participants completed Peters’ (1998) handedness questionnaire followed by the self-report BIS/BAS Scale. Significant effects of both handedness and gender on the BIS scores were found, with left-handers and females scoring significantly higher on inhibition. BIS scores were re-examined to include FFFS scores, which showed a significant effect of gender. Revised BIS scores replicated the original BIS findings. These findings are discussed in relation to handedness research
To be or not to Be? - First Evidence for Neutrinoless Double Beta Decay
Double beta decay is indispensable to solve the question of the neutrino mass
matrix together with oscillation experiments. Recent analysis of the most
sensitive experiment since nine years - the HEIDELBERG-MOSCOW experiment in
Gran-Sasso - yields a first indication for the neutrinoless decay mode. This
result is the first evidence for lepton number violation and proves the
neutrino to be a Majorana particle. We give the present status of the analysis
in this report. It excludes several of the neutrino mass scenarios allowed from
present neutrino oscillation experiments - only degenerate scenarios and those
with inverse mass hierarchy survive. This result allows neutrinos to still play
an important role as dark matter in the Universe. To improve the accuracy of
the present result, considerably enlarged experiments are required, such as
GENIUS. A GENIUS Test Facility has been funded and will come into operation by
early 2003.Comment: 16 pages, latex, 10 figures, Talk was presented at International
Conference "Neutrinos and Implications for Physics Beyond the Standard
Model", Oct. 11-13, 2002, Stony Brook, USA, Proc. (2003) ed. by R. Shrock,
also see Home Page of Heidelberg Non-Accelerator Particle Physics Group:
http://www.mpi-hd.mpg.de/non_acc
Free relative constructions in OT syntax
This paper is part of a research project on OT Syntax and the typology of the free relative (FR) construction. It concentrates on the details of an OT analysis and some of its consequences for OT syntax. I will not present a general discussion of the phenomenon and the many controversial issues it is famous for in generative syntax
Learning labelled dependencies in machine translation evaluation
Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate
better than other existing metrics with human judgements. Other research in this area has presented machine learning
methods which learn directly from human judgements. In this paper, we present a novel combination of dependency- and
machine learning-based approaches to automatic MT evaluation, and demonstrate greater correlations with human judgement than the existing state-of-the-art methods.
In addition, we examine the extent to which our novel method can be generalised across different tasks and domains
MixUp as Locally Linear Out-Of-Manifold Regularization
MixUp is a recently proposed data-augmentation scheme, which linearly
interpolates a random pair of training examples and correspondingly the one-hot
representations of their labels. Training deep neural networks with such
additional data is shown capable of significantly improving the predictive
accuracy of the current art. The power of MixUp, however, is primarily
established empirically and its working and effectiveness have not been
explained in any depth. In this paper, we develop an understanding for MixUp as
a form of "out-of-manifold regularization", which imposes certain "local
linearity" constraints on the model's input space beyond the data manifold.
This analysis enables us to identify a limitation of MixUp, which we call
"manifold intrusion". In a nutshell, manifold intrusion in MixUp is a form of
under-fitting resulting from conflicts between the synthetic labels of the
mixed-up examples and the labels of original training data. Such a phenomenon
usually happens when the parameters controlling the generation of mixing
policies are not sufficiently fine-tuned on the training data. To address this
issue, we propose a novel adaptive version of MixUp, where the mixing policies
are automatically learned from the data using an additional network and
objective function designed to avoid manifold intrusion. The proposed
regularizer, AdaMixUp, is empirically evaluated on several benchmark datasets.
Extensive experiments demonstrate that AdaMixUp improves upon MixUp when
applied to the current art of deep classification models.Comment: Accepted by AAAI201
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