If the evidence are observed on the basis of their source, they can be classified on material and personal evidence. If the source of evidence is the object or trace of the criminal offense, then ...material evidence is discussed. The core of evidence is the detection, execution, verification, and evaluation of evidence. The author tries to classify the material evidence in order to present the basic characteristics of each of them. The aim of this paper is to indicate the legal provisions on the basis of which all actions are undertaken in connection with this type of evidence, but also to emphasize the importance of material evidence which, due to technologies offering great technical possibilities of analysis, can be essential for the procedure itself. It should also be noted that in spite of the enormous importance of material evidence in contemporary criminal proceedings, errors in the execution itself can occur, but also the actions in which they are used which can never be excluded, and therefore the material evidence can not be considered as absolutely superior. The process of proofing is carried out by insight into material evidence, but material evidence is the subject of various types of expertise, from which it can be concluded that they become the basis for determining the facts of the criminal proceedings.
The Bayesian Learning for Neural Networks (BLNN) package coalesces the predictive power of neural networks with a breadth of Bayesian sampling techniques for the first time in R. BLNN offers users ...Hamiltonian Monte Carlo (HMC) and No-U-Turn (NUTS) sampling algorithms with dual averaging for posterior weight generation. A robust implementation of hyper-parameters and optional re-estimation through the evidence procedure gives BLNN high predictive precision. BLNN is compatible with RStan diagnostic tool ShinyStan. BLNN can be used in a wide range of applications which are based on developing statistical models such as multiple linear and logistic regression, classification, and survival analysis.
A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in ...the regression model with an individual regularization parameter, the ability for the orthogonal least squares model selection to produce a very sparse model with good generalization performance is greatly enhanced. Furthermore, with the assistance of local regularization, when to terminate the model selection procedure becomes much clearer. A comparison with a state-of-the-art method for constructing sparse regression models, known as the relevance vector machine, is given. The proposed LROLS algorithm is shown to possess considerable computational advantages, including well conditioned solution and faster convergence speed.
Echo state network (ESN) is viewed as a temporal expansion which naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the ...generated echo-regressors effectively explain the teacher output and we propose to determine the importance of the echo-regressors by a joint calculation of the individual variance contributions and Bayesian relevance using the locally regularized orthogonal forward regression (LROFR). This information can be advantageously used in a variety of ways for an analysis of an ESN structure. We present a locally regularized linear readout built using LROFR. The readout may have a smaller dimensionality than the ESN model itself, and improves robustness and accuracy of an ESN. Its main advantage is ability to determine what type of an additional readout is suitable for a task at hand. Comparison with PCA is provided too. We also propose a radial basis function (RBF) readout built using LROFR, since flexibility of the linear readout has limitations and might be insufficient for complex tasks. Its excellent generalization abilities make it a viable alternative to feed-forward neural networks or relevance-vector-machines. For cases where more temporal capacity is required we propose well studied delay&sum readout.
Request for protection of legality Bugarski, Tatjana
Zbornik radova (Pravni fakultet u Novom Sadu),
2016, Volume:
50, Issue:
1
Journal Article
Peer reviewed
Open access
The author of this work, attention to the request for protection of legality as an extraordinary legal remedy which has significantly modified the latest Code of Criminal Procedure (2011). This ...extraordinary remedy is an instrument that ensures the rule of law in criminal proceedings and the dam is a kind of legality and constitutionality of the actions and decisions of the judicial authorities. As an important mechanism to elimination of illegality in criminal proceedings, including the illegality in connection with the execution of evidentiary actions, the author's attention to the analysis of positive solutions in our criminal procedural legislation in connection with a request for protection of legality and compared with certain comparative solutions. Special attention is devoted to certain contentious issues regarding this extraordinary remedy, and jurisprudence. .
A hybrid computational system, composed of the finite element method (FEM) and cascade neural network system (CNNs), is applied to the identification of three geometrical parameters of elastic ...arches, i.e. span
l, height
f and cross-sectional thickness
h. FEM is used in the direct (forward) analysis, which corresponds to the mapping
α
=
{
l,
f,
h}
→
{
ω
j
}, where:
α – vector of control parameters,
ω
j
– arch eigenfrequencies. The reverse analysis is related to the identification procedure in which the reverse mapping is performed {
ω
j
}
→
{
α
i}. For the identification purposes a recurrent, three level CNNs of structure (
D
k
-
H
k
-1)
s
was formulated, where:
k – recurrence step,
s
=
I, II, III-levels of cascade system. The Semi-Bayesian approach is introduced for the design of CNNs applying the MML Maximum Marginal Likelihood) criterion. The computation of hyperparameters is performed by means of the Bayesian procedure evidence. The numerical analysis proves a great numerical efficiency of the proposed hybrid approach for both the perfect (noiseless) values of eigenfrequencies and noisy ones simulated by an added artificial noise.
If a first instance department of EPO does not take up a party's offer to supply a witness, in certain circumstances this can amount to a substantial procedural violation.
The paper pertains to the comprehensive amendment to the Polish Code of Civil Procedure of 4 July 2019, which covered, among others, the regulations concerning evidence in civil proceedings. The ...amendment influenced all the aspects of evidence procedure: means of evidence, taking of evidence, as well as its assessment. The author attempted to analyse the amended provisions through the essence of the influence that the evidence procedure has on the entire court examination proceedings, and in particular whether the amendment introduced any provisions improving the dynamics of civil procedure.
Reconfigurable logic (FPGA) allows to implement custom-precision arithmetic units. In this work we propose an algorithm, which employs a Bayesian technique to determine the optimal amount of bits for ...representing the involved continuous variables. We restrict ourselves to the problem of nonlinear approximation, where an assumed data model consists of superimposed signals with unknown parameters. By fitting such models using a variational Bayesian EM-based algorithm, we can determine the importance of each signal component using a techniques inspired by the Bayesian evidence procedure. Due to the structure of the obtained variational update expressions, it becomes possible to show that the evidence value represents the combined effect of the relevance of a signal component for explaining the measurement data, and additive noise, associated with this component. This insight allows to interpret the value of the evidence parameters in terms of a Signal-to-Noise ratio, which is then used to develop an optimal discretization scheme. The effectiveness of the proposed approach is demonstrated with two synthetic examples, showing a bitwidth reduction of more than 70% at the cost of a relative mean squared error of 0.0036 and 0.012, respectively.
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