236 research outputs found
Claims to ignorance as a form of participation in transitional justice
© The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).Transitional justice is premised on participation that allows local publics to construct, critique and have some ownership over the process. The current scholarship assumes that individuals openly express their views of the process, or that they remain silent. The scholarship has neglected a third, significant form of participation: active withholding of views by saying “I don’t know”. This article examines such claims to ignorance and argues that they can provide insight into participation. While both qualitative and quantitative researchers of transitional justice have observed a pervasive pattern of high “don’t know” responses, such claims to ignorance have not been studied. This article develops a theoretical framework that shows that “don’t know” responses are a valuable source of information and argues that they are often an expression of a lack of willingness to respond, rather than genuine ignorance. Drawing on an original corpus of data collected through inter-ethnic focus groups and surveys conducted in four former Yugoslav countries, the study demonstrates how claims to ignorance are constructed as novel manifestations of resistance, restraint or disentitlement. These point to a rejection of transitional justice, which needs to be addressed if individuals are to feel like legitimate participants in the process.Peer reviewedFinal Published versio
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
This paper considers the classification of linear subspaces with mismatched
classifiers. In particular, we assume a model where one observes signals in the
presence of isotropic Gaussian noise and the distribution of the signals
conditioned on a given class is Gaussian with a zero mean and a low-rank
covariance matrix. We also assume that the classifier knows only a mismatched
version of the parameters of input distribution in lieu of the true parameters.
By constructing an asymptotic low-noise expansion of an upper bound to the
error probability of such a mismatched classifier, we provide sufficient
conditions for reliable classification in the low-noise regime that are able to
sharply predict the absence of a classification error floor. Such conditions
are a function of the geometry of the true signal distribution, the geometry of
the mismatched signal distributions as well as the interplay between such
geometries, namely, the principal angles and the overlap between the true and
the mismatched signal subspaces. Numerical results demonstrate that our
conditions for reliable classification can sharply predict the behavior of a
mismatched classifier both with synthetic data and in a motion segmentation and
a hand-written digit classification applications.Comment: 17 pages, 7 figures, submitted to IEEE Transactions on Signal
Processin
Impact of public expenditure in labour market policies and other selected factors on youth unemployment
This study investigates the impact of selected macroeconomic,
demographic, institutional and educational determinants on youth
unemployment rates in Europe, with special attention to effects of
Active Labour Market Policies on unemployment dynamics. Dynamic
panel data estimates have been done with the Generalised Method
of Moments on data from 27 E.U. Members States plus Norway
(2005–2014). The results indicate significant impact of the main
macroeconomic variables on youth unemployment rates, total
unemployment rates and shares of young people (15–24 y.o.) neither
employed nor in education or training. Other variables show various
levels of significance, including variables which describe labour
market policies (L.M.P.s). In all estimations, public expenditure in
L.M.P.s as a percentage of gross domestic product has statistically
significant impact on unemployment rates, with positive coefficients.
However, opposite effects have been estimated when using the
number of participants in L.M.P.s and public expenditure in L.M.P.s
per unemployed person, which suggests that L.M.P.s better target
the general unemployed population than the vulnerable group of
the unemployed youth
Assessment of Immature Platelet Fraction in the Diagnosis of Wiskott-Aldrich Syndrome.
Children with Wiskott-Aldrich syndrome (WAS) are often first diagnosed with immune thrombocytopenia (ITP), potentially leading to both inappropriate treatment and the delay of life-saving definitive therapy. WAS is traditionally differentiated from ITP based on the small size of WAS platelets. In practice, microthrombocytopenia is often not present or not appreciated in children with WAS. To develop an alternative method of differentiating WAS from ITP, we retrospectively reviewed all complete blood counts and measurements of immature platelet fraction (IPF) in 18 subjects with WAS and 38 subjects with a diagnosis of ITP treated at our hospital. Examination of peripheral blood smears revealed a wide range of platelet sizes in subjects with WAS. Mean platelet volume (MPV) was not reported in 26% of subjects, and subjects in whom MPV was not reported had lower platelet counts than did subjects in whom MPV was reported. Subjects with WAS had a lower IPF than would be expected for their level of thrombocytopenia, and the IPF in subjects with WAS was significantly lower than in subjects with a diagnosis of ITP. Using logistic regression, we developed and validated a rule based on platelet count and IPF that was more sensitive for the diagnosis of WAS than was the MPV, and was applicable regardless of the level of platelets or the availability of the MPV. Our observations demonstrate that MPV is often not available in severely thrombocytopenic subjects, which may hinder the diagnosis of WAS. In addition, subjects with WAS have a low IPF, which is consistent with the notion that a platelet production defect contributes to the thrombocytopenia of WAS. Knowledge of this detail of WAS pathophysiology allows to differentiate WAS from ITP with increased sensitivity, thereby allowing a physician to spare children with WAS from inappropriate treatment, and make definitive therapy available in a timely manner
Heroes, Courts and Normative Clashes: The Effects of Transitional Justice on Norms and Narratives in Croatia
This thesis investigates the expressivist, or extra-legal, effects of the transitional justice process in Croatia that began following the 1991-1995 conflict. It analyses how international and domestic war crimes trials, as well as civil society efforts, have led to deliberation over norms and narratives related to the war. This is based on a deliberative understanding of the transitional justice process, which focuses on the potential for trials to initiate public deliberation that involves multiple representations of the past. The primary method of data collection was focus groups with teachers, pensioners and members of war veterans' groups across several locations, while follow-up interviews, a brief survey and Qualitative Comparative Analysis (QCA) were used to verify results. The results of the analysis question theories of human rights norm cascades, since in the case of Croatia stronger "everyday" narratives have undermined the trickle-down effects of transitional justice narratives advocated by authorities. Chief among these highly trusted "everyday" narratives is the predominant Croatian war narrative, one of defence against a larger Serbian aggressor, which permeates across Croatian society and aspects of which are not questioned at all. The effect of this is that transitional justice efforts work in an atmosphere of cynicism and distrust with institutions, unless their narrative is in line with "everyday" expectations, and they therefore struggle to compete with personal and local narratives. These narratives strongly affect how the Croatian public understands the rule of law and history, as well as how it regards the Serb minority in the state
The use of satellite remote sensing to determine the spatial and temporal distribution of surface water on the eastern shores of Lake St. Lucia.
Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2006.The Eastern Shores of Lake St Lucia forms part of the ecologically important Greater St Lucia Wetland Park, designated a World Heritage Site in 1999. The landscape is characterised by surface water, a high water table and numerous wetlands. Little is known about the distribution and temporal fluctuations of this surface water and its relationship to the wetlands. This study uses remote sensing to examine the relationship by mapping the extent of seasonal, ephemeral and permanent surface water on the Eastern Shores. Much of the surface water occurs in conjunction with emergent vegetation and is not easily mapped using hard classification methods. Neither a cluster analysis nor a maximum likelihood classification were able to map the subtle variations of the water-vegetation mix. Much more successful was the application of spectral mixture analysis using image endmembers of water, woody vegetation and non-woody vegetation. This technique was applied to seven Landsat Thematic Mapper images from 1991, 2001 and 2002. Steep slopes, forests and bare sand were masked out prior to classification. Maps of water extent were produced for each of the seven study dates. Mapping accuracy was verified against rainfall, with high correlations being obtained against rainfall accumulated over six months and longer. Long-term rainfall patterns were reflected in the surface water distribution, with inundation being more extensive when accumulated rainfall was high. Fire scars reduced the accuracy of the spectral mixture analysis but these scars could be identified from the thermal image bands. The largest open water body in the study area was Lake Bhangazi. Large extents of surface water were also found in the Mfabeni swamp and the wilderness area to the north where water concentrations of 90% were measured during wet periods. Surface water present near Brodies Crossing during wet periods was less evident when rainfall was lower. No inundation was recorded in the areas to the west and south-west of the Mfabeni swamp or in the southern parts of the study area. The techniques used in this study were developed into a water mapping protocol that uses image endmembers and spectral mixture analysis to measure water concentration
Robust Large Margin Deep Neural Networks
The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization reparametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED, and ImageNet datasets
Lessons from the Rademacher complexity for deep learning
Understanding the generalization properties of deep learning models is critical for
successful applications, especially in the regimes where the number of training
samples is limited. We study the generalization properties of deep neural networks
via the empirical Rademacher complexity and show that it is easier to control the
complexity of convolutional networks compared to general fully connected networks.
In particular, we justify the usage of small convolutional kernels in deep
networks as they lead to a better generalization error. Moreover, we propose a
representation based regularization method that allows to decrease the generalization
error by controlling the coherence of the representation. Experiments on the
MNIST dataset support these foundations
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