Hesaplamalı Hukuk

İnternetin ve bilişim teknolojilerinin gelişimi veriye erişimi kolaylaştırmış, verilerin niteliksel ve niceliksel olarak büyümesi söz konusu olmuştur. Tüm bu gelişmeler, veri analizlerinin yaygınlaşmasını sağlamıştır. Gelişen teknoloji ile farklı analiz sistemlerinin veri analizlerinde kullanılarak elde edilen sonuçlara göre öngörülen hipotezlerin doğruluğunun veya yasal düzenlemelerin etkinliğinin veya etkili olma ihtimalinin değerlendirilebildiği uygulamalar gündeme gelmiştir. Bu uygulamalarda hukukun sayılarla ifade edilerek yapılan analizler olarak tanımlanabilen hesaplamalı hukuk neticesinde karmaşıklık teorisi yardımıyla mevzuatın uygulamaya yansıması dijital sistemler üzerinden sorgulanmaktadır. Burada amaç hukuk uygulamasındaki somut gerçeklerin tespit edilmesidir. Bu sorgular neticesinde elde edilen çıktılara göre ilgili hukuk kurallarının özgülendiği amacın uygulamada etkili olup olmadığı değerlendirilebilmektedir. Benzer sistemler kullanılarak hukuki düzenlemelerin birbiri ile ilişkisi yahut bir kuralın hukuk sistemi içerisindeki konumu da analiz edilebilmektedir. Toplumların davranışları, suça yönelimleri gibi sosyal olguların da hesaplamalı hukukta analizi mümkün olup bu analiz sonuçları doğrultusunda suçu önleyici mekanizmalar öngörülebilmektedir. Bu çalışmada hesaplamalı hukukun tanımı ve ağ analizi, ampirik analizi ve algoritmik hukuk gibi uygulama alanları analiz edilmiştir. Hesaplamalı hukuk uygulamalarının irdelendiği örnekler üzerinden muhtemel riskleri de belirtilerek hukuki işlevselliğin artırılmasında hesaplamalı hukukun öneminin ortaya konması amaçlanmıştır. Bu çalışmanın hesaplamalı hukukun Türk hukukunda uygulanması ve doktrin oluşmasında tetikleyici olacağı öngörülmektedir

T\mathbb{T}-Operator Limits on Optical Communication: Metaoptics, Computation, and Input-Output Transformations

We present an optimization framework based on Lagrange duality and the scattering $\mathbb{T}$ operator of electromagnetism to construct limits on the possible features that may be imparted to a collection of output fields from a collection of input fields, i.e., constraints on achievable optical transformations and the characteristics of structured materials as communication channels. Implications of these bounds on the performance of representative optical devices having multi-wavelength or multiport functionalities are examined in the context of electromagnetic shielding, focusing, near-field resolution, and linear computing

Greed is Good for Deterministic Scale-Free Networks

Large real-world networks typically follow a power-law degree distribution. To study such networks, numerous random graph models have been proposed. However, real-world networks are not drawn at random. In fact, the behavior of real-world networks and random graph models can be the complete opposite of one another, depending on the considered property. Brach, Cygan, Lacki, and Sankowski [SODA 2016] introduced two natural deterministic conditions: (1) a power-law upper bound on the degree distribution (PLB-U) and (2) power-law neighborhoods, that is, the degree distribution of neighbors of each vertex is also upper bounded by a power law (PLB-N). They showed that many real-world networks satisfy both deterministic properties and exploit them to design faster algorithms for a number of classical graph problems like transitive closure, maximum matching, determinant, PageRank, matrix inverse, counting triangles and maximum clique. We complement the work of Brach et al. by showing that some well-studied random graph models exhibit both the mentioned PLB properties and additionally also a power-law lower bound on the degree distribution (PLB-L). All three properties hold with high probability for Chung-Lu Random Graphs and Geometric Inhomogeneous Random Graphs and almost surely for Hyperbolic Random Graphs. As a consequence, all results of Brach et al. also hold with high probability for Chung-Lu Random Graphs and Geometric Inhomogeneous Random Graphs and almost surely for Hyperbolic Random Graphs. In the second part of this work we study three classical NP-hard combinatorial optimization problems on PLB networks. It is known that on general graphs, a greedy algorithm, which chooses nodes in the order of their degree, only achieves an approximation factor of asymptotically at least logarithmic in the maximum degree for Minimum Vertex Cover and Minimum Dominating Set, and an approximation factor of asymptotically at least the maximum degree for Maximum Independent Set. We prove that the PLB-U property suffices such that the greedy approach achieves a constant-factor approximation for all three problems. We also show that all three combinatorial optimization problems are APX-complete, even if all PLB-properties hold. Hence, a PTAS cannot be expected, unless P=NP

Investigation into experimental toxicological properties of plant protection products having a potential link to Parkinson's disease and childhood leukaemia

In 2013, EFSA published a literature review on epidemiological studies linking exposure to pesticides and human health outcome. As a follow up, the EFSA Panel on Plant Protection Products and their residues (PPR Panel) was requested to investigate the plausible involvement of pesticide exposure as a risk factor for Parkinson's disease (PD) and childhood leukaemia (CHL). A systematic literature review on PD and CHL and mode of actions for pesticides was published by EFSA in 2016 and used as background documentation. The Panel used the Adverse Outcome Pathway (AOP) conceptual framework to define the biological plausibility in relation to epidemiological studies by means of identification of specific symptoms of the diseases as AO. The AOP combines multiple information and provides knowledge of biological pathways, highlights species differences and similarities, identifies research needs and supports regulatory decisions. In this context, the AOP approach could help in organising the available experimental knowledge to assess biological plausibility by describing the link between a molecular initiating event (MIE) and the AO through a series of biologically plausible and essential key events (KEs). As the AOP is chemically agnostic, tool chemical compounds were selected to empirically support the response and temporal concordance of the key event relationships (KERs). Three qualitative and one putative AOP were developed by the Panel using the results obtained. The Panel supports the use of the AOP framework to scientifically and transparently explore the biological plausibility of the association between pesticide exposure and human health outcomes, identify data gaps, define a tailored testing strategy and suggests an AOP’s informed Integrated Approach for Testing and Assessment (IATA)

Adapted simplification of complex real-world networks

Networks are an important tool for analyzing and visualizing different complex systems. Examples of real-world networks include social network of friends on Facebook, technological network of railways, biological network of interactions between proteins and information networks of hyperlinks between the Web pages. The evolution of the Web and the capability of storing large amounts of data have caused the size of networked systems and their complexity to increase. However, the algorithms for network analysis and visualization appear impractical for addressing very large systems. Furthermore, data about networks are not always complete, their structure may be hidden, or they may change quickly over time. Any network studied in the literature is thus inevitably just a simplified representative of its real-world analogue. For these reasons, understanding how an incomplete system differs from a complete one is crucial. Recently, a number of techniques have been proposed for simplifying complex networks. The simplification is a process, which reduce the size of a large network with merging, sampling or exploration of nodes or links in a network. Simplification techniques are applied to large networks to allow for their faster and more efficient analysis. Since the findings of the analyses and simulations of simplified networks are implied for the original ones, it is of key importance to understand the structural differences between the original networks and their simplified variants. Network simplification has been extensively investigated from different perspectives. A large number of studies focus on the changes in network properties introduced by simplification. On the other hand, only a few studies compare simplification techniques. In this doctoral thesis, we study the changes of real-world networks introduced by simplification and analyze the differences among simplification techniques. We propose an approach for assessing the effectiveness of simplification. Based on the similarity between original and simplified networks, we compare different simplification techniques. We simplify a number of real-world networks of various types and sizes and explore the preservation of network properties on simplified networks of different sizes. We analyze the changes of network density under the simplification and compare characteristic groups of nodes in original and simplified networks. Based on the findings of the analyses we introduce the scheme for choosing the appropriate simplification technique for a particular network

Dimension-reduction and discrimination of neuronal multi-channel signals

Dimensionsreduktion und Trennung neuronaler Multikanal-Signale

Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation

peer reviewedAffect decoding through brain-computer interfacing (BCI) holds great potential to capture users’ feelings and emotional responses via non-invasive electroencephalogram (EEG) sensing. Yet, little research has been conducted to understand efficient decoding when users are exposed to dynamic audiovisual contents. In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal length, window size for feature extraction, and frequency bands. We train both classic Machine Learning models (SVMs and k-NNs) and modern Deep Learning models (FCNNs and GTNs). Our results show that: (1) affect can be effectively decoded using less than 1 minute of EEG signal; (2) temporal windows of 6 and 10 seconds provide the best classification performance for classic Machine Learning models but Deep Learning models benefit from much shorter windows of 2 seconds; and (3) any model trained on the Beta band alone achieves similar (sometimes better) performance than when trained on all frequency bands. Taken together, our results indicate that affect decoding can work in more realistic conditions than currently assumed, thus becoming a viable technology for creating better interfaces and user models