314 research outputs found
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, leading to overfitting and poor generalization abilities. In this
paper, we suggest the use of the mutual information measure to select variables
from the initial set. The mutual information measures the information content
in input variables with respect to the model output, without making any
assumption on the model that will be used; it is thus suitable for nonlinear
modelling. In addition, it leads to the selection of variables among the
initial set, and not to linear or nonlinear combinations of them. Without
decreasing the model performances compared to other variable projection
methods, it allows therefore a greater interpretability of the results
A Theoretical Framework for Target Propagation
The success of deep learning, a brain-inspired form of AI, has sparked
interest in understanding how the brain could similarly learn across multiple
layers of neurons. However, the majority of biologically-plausible learning
algorithms have not yet reached the performance of backpropagation (BP), nor
are they built on strong theoretical foundations. Here, we analyze target
propagation (TP), a popular but not yet fully understood alternative to BP,
from the standpoint of mathematical optimization. Our theory shows that TP is
closely related to Gauss-Newton optimization and thus substantially differs
from BP. Furthermore, our analysis reveals a fundamental limitation of
difference target propagation (DTP), a well-known variant of TP, in the
realistic scenario of non-invertible neural networks. We provide a first
solution to this problem through a novel reconstruction loss that improves
feedback weight training, while simultaneously introducing architectural
flexibility by allowing for direct feedback connections from the output to each
hidden layer. Our theory is corroborated by experimental results that show
significant improvements in performance and in the alignment of forward weight
updates with loss gradients, compared to DTP.Comment: 13 pages and 4 figures in main manuscript; 41 pages and 8 figures in
supplementary materia
A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis
Prediction problems from spectra are largely encountered in chemometry. In
addition to accurate predictions, it is often needed to extract information
about which wavelengths in the spectra contribute in an effective way to the
quality of the prediction. This implies to select wavelengths (or wavelength
intervals), a problem associated to variable selection. In this paper, it is
shown how this problem may be tackled in the specific case of smooth (for
example infrared) spectra. The functional character of the spectra (their
smoothness) is taken into account through a functional variable projection
procedure. Contrarily to standard approaches, the projection is performed on a
basis that is driven by the spectra themselves, in order to best fit their
characteristics. The methodology is illustrated by two examples of functional
projection, using Independent Component Analysis and functional variable
clustering, respectively. The performances on two standard infrared spectra
benchmarks are illustrated.Comment: A paraitr
Real-time visualization of a sparse parametric mixture model for BTF rendering
Bidirectional Texture Functions (BTF) allow high quality visualization of real world materials exhibiting complex appearance and details that can not be faithfully represented using simpler analytical or parametric representations. Accurate representations of such materials require huge amounts of data, hindering real time rendering. BTFs compress the raw original data, constituting a compromise between visual quality and rendering time. This paper presents an implementation of a state of the art BTF representation on the GPU, allowing interactive high fidelity visualization of complex geometric models textured with multiple BTFs. Scalability with respect to the geometric complexity, amount of lights and number of BTFs is also studied.Fundação para a Ciência e Tecnologi
Sparse multinomial kernel discriminant analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets
The European Union approach to flood risk management and improving societal resilience: lessons from the implementation of the Floods Directive in six European countries
Diversity in flood risk management approaches is often considered to be a strength. However in some national settings, and especially for transboundary rivers, variability and the incompatibility of approaches can reduce the effectiveness of flood risk management. Placed in the context of increasing flood risks, as well as the potential for flooding to undermine the European Union's sustainable development goals, a desire to increase societal resilience to flooding has prompted the introduction of a common European Framework. This paper provides a legal and policy analysis of the implementation of the Floods Directive (2007/60/EC) in six countries; Belgium (Flemish Region), England, France, the Netherlands, Poland and Sweden. Evaluation criteria from existing legal and policy literature frame the study of the Directive and its impact on enhancing or constraining societal resilience by using an adaptive governance approach. These criteria are initially used to analyze the key components of the EU approach, before providing insight of the implementation of the Directive at a national level. Similarities and differences in the legal translation of European goals into existing flood risk management are analyzed alongside their relative influence on policy and practice. The research highlights that the impact of the Floods Directive on increasing societal resilience has been nationally variable, in part due to its focus on procedural obligations, rather than on more substantive requirements. Analysis shows that despite a focus on transboundary river basin management, in some cases existing traditions of flood risk management, have overridden objectives to harmonize flood risk management. This could be strengthened by requiring more stringent cooperation and providing the competent authorities in International River Basins Districts with more power. Despite some shortcomings in directly impacting flood risk outcomes, the Directive has positively stimulated discussion and flood risk management planning in countries that were perhaps lagging behind
Explaining Support Vector Machines: A Color Based Nomogram.
PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. OBJECTIVE: In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. RESULTS: Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. CONCLUSIONS: This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method
Assessing the legitimacy of flood risk governance arrangements in Europe: insights from intra-country evaluations
Legitimacy has received comparatively less attention than societal resilience in the context of flooding, thus methods for assessing and monitoring the legitimacy of Flood Risk Governance Arrangements (FRGA) are noticeably lacking. This study attempts to address this gap by assessing the legitimacy of FRGAs in six European countries through cross-disciplinary and comparative research methods. On the basis of this assessment, recommendation
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