What makes the securities criminal law system of the United States work? ‘All-embracing’ ‘blanket’ securities crimes and the linked enforcement framework

The article explores the key factors that make the securities criminal law of the United States (US), as one of the integral building blocks of the capital markets and securities regulatory system, efficient. This includes the role and characteristics of sectoral (blanket) all-embracing securities crimes enshrined into the federal securities statutes, their nexus with general crimes, the close cooperation of the Securities Exchange Commission (SEC) and prosecutorial offices, the applicableevidentiary standards, and the fundamental policies undergirding these laws. The rich repository of US experiences should be instructive not only to the Member States of the European Union (EU) striving to forge deeper capital markets but also to those endeavoring to accede the EU (e.g., Serbia), or to create deep capital markets for which efficient prosecution of securities crimes is inevitable.http://www.pravnizapisi.rs/en

Kada je i zašto “pečat” Europske unije nedovoljan? - Upozorenja sustavima na putu prema Europskoj uniji

Due to the principle of subsidiarity, European Union law is inherently incomplete. Hence, neither the transposition of the acquis communautaire, nor the law or impetuses coming from Brussels is a panacea to numerous real-life legal, economic or political problems not being focused upon by the Union. This is often forgotten not just by countries approaching the Union but also by the Members States. The article is a review of a number of such legal and economic challenges faced in particular by Central European and ex-Yugoslav successor states, from such new transplants as franchise through risks of the pyramid and Ponzi schemes from the realms of financial law.Zbog primjene načela supsidijarnosti pravo Europske unije u svojoj je naravi nepotpuno. Stoga ni prijenos i usvajanje acquis communautairea, ni pravo i poticaji koji dolaze iz Bruxellesa nisu rješenje za mnogobrojne, svakodnevne pravne, ekonomske i političke probleme koji nisu u fokusu interesa Europske unije. To se često zaboravlja ne samo u zemljama pristupnicama već i u zemljama članicama. U radu je analiziran niz takvih pravnih i ekonomskih izazova s kojima su posebice suočene države jugoistočne Europe i države sljednice bivše Jugoslavije. Iako su primjeri ponajprije iz sfera građanskog i trgovačkog prava, izabrani slučajevi imaju i javnopravnu dimenziju. U vezi s rastućom, no istodobno i vrlo upitnom, djelatnošću novih agencija za naplatu dugova u regiji, na primjer, može se postaviti legitimno pitanje ustavnopravne naravi tko snosi odgovornost za izostanak regulacije. Isto vrijedi i za toleriranje tako velikih problema poput opće nelikvidnosti s lančanim učinkom. Razlozi, povezani rizici, regulatorne reakcije (ako postoje) i poznata rješenja razlikuju se s obzirom na svaki od izloženih problema. Dok je većina postsocijalističkih država regije reformirala svoja prava realnog osiguranja tražbina (založno pravo) i još su u postupku prilagodbe novih instituta nadahnutih praksom common lawa, franšiza je gotovo neprimjetno postala jedan od najpopularnijih složenijih ugovora i poslovnih modela u regiji, iako ne svuda u istoj mjeri. U pogledu franšize ne samo da je izostala reakcija zakonodavca, već nedostaje i jasan odgovor na neka temeljna pitanja ugovora poput onoga je li nužna asimetričnost pripadajuća poslovnom modelu franchisinga u skladu s općim načelima privatnog, konkretnije građanskog prava. Slučajevi piramidalnih prijevara i Ponzi shema instruktivni su zbog drugih razloga: nijedna postsocijalistička država nije izbjegla pojavu tih patoloških financijskih fenomena, a zbog izostanka odgovarajuće regulacije i sankcije postoji tendencija njihova ponovnog javljanja u promijenjenim oblicima. Kako se oni uobičajeno ne smatraju problemom u nadležnosti pravnika, zakonodavci, regulatori i suci koji su suočeni s ovim vješto prikrivenim poslovnim modelima ne mogu primjereno reagirati. Zajednički nazivnik svih ovih problema i pojava jest da oni nisu regulirani pravom Europske unije te stoga ni bilo kakvo rješenje za njih nije ponuđeno tim pravom. Svaka jurisdikcija, pravni sustav određene države, ostavljena je stoga da sama pronalazi i primjenjuje primjerene pravne mehanizme radi zaštite od tih pojava i borbe s njima

New voting functions for neural network algorithms

Neural Network and Convolutional Neural Network algorithms are among the best performing machine learning algorithms. However, the performance of the algorithms may vary between multiple runs because of the stochastic nature of these algorithms. This stochastic behavior can result in weaker accuracy for a single run, and in many cases, it is hard to tell whether we should repeat the learning giving a chance to have a better result. Among the useful techniques to solve this problem, we can use the committee machine and the ensemble methods, which in many cases give better than average or even better than the best individual result. We defined new voting function variants for ensemble learner committee machine algorithms which can be used as competitors of the well-known voting functions. Some belong to the locally weighted average voting functions, others are meta voting functions calculated from the output of the previous voting functions functions called with the results of the individual learners. The performance evaluation of these methods was done from numerous learning sessions

Fuzzification of training data class membership binary values for neural network algorithms

We propose an algorithm improvement for classifying machine learning algorithms with the fuzzification of training data binary class membership values. This method can possibly be used to correct the training data output values during the training. The proposed modification can be used for algorithms running individual learners and also as an ensemble method for multiple learners for better performance. For this purpose, we define the single and the ensemble variants of the algorithm. Our experiment was done using convolutional neural network (CNN) classifiers for the base of our proposed method, however, these techniques might be used for other machine learning classifiers as well, which produce fuzzy output values. This fuzzification starts with using the original binary class membership values given in the dataset. During training these values are modified with the current knowledge of the machine learning algorithm