Researching ‘bogus’ asylum seekers, ‘illegal’ migrants and ‘crimmigrants’

Both immigration and criminal laws are, at their core, systems of inclusion and exclusion. They are designed to determine whether and how to include individuals as members of society or exclude them from it, thereby, creating insiders and outsiders (Stumpf 2006). Both are designed to create distinct categories of people — innocent versus guilty, admitted versus excluded or, as majority would say, ‘legal’ versus ‘illegal’ (Stumpf 2006). Viewed in that light, perhaps it is not surprising that these two areas of law have become inextrica- bly connected in the official discourses. When politicians and policy makers (and also law enforcement authorities and tabloid press) seek to raise the barriers for non-citizens to attain membership in society, it is unremarkable that they turn their attention to an area of the law that similarly func- tions to exclude the ‘other’ — transforming immigrants into ‘crimmigrants’.1 As a criminological researcher one then has to rise up to the challenges of disentangling these so-called officially constructed (pseudo) realities, and breaking free from a continued dominance of authoritative discourses, and developing an alternative understanding of ‘crimmigration’ by connecting the processes of criminal is ation and ‘other ing’ with poverty, xe no-racism and other forms of social exclusion (see Institute of Race Relations 1987; Richmond 1994; Fekete 2001; Bowling and Phillips 2002; Sivanandan 2002; Weber and Bowling 2004)

Information Horizons in Networks

We investigate and quantify the interplay between topology and ability to send specific signals in complex networks. We find that in a majority of investigated real-world networks the ability to communicate is favored by the network topology on small distances, but disfavored at larger distances. We further discuss how the ability to locate specific nodes can be improved if information associated to the overall traffic in the network is available.Comment: Submitted top PR

Null vectors, 3-point and 4-point functions in conformal field theory

We consider 3-point and 4-point correlation functions in a conformal field theory with a W-algebra symmetry. Whereas in a theory with only Virasoro symmetry the three point functions of descendants fields are uniquely determined by the three point function of the corresponding primary fields this is not the case for a theory with $W_3$ algebra symmetry. The generic 3-point functions of W-descendant fields have a countable degree of arbitrariness. We find, however, that if one of the fields belongs to a representation with null states that this has implications for the 3-point functions. In particular if one of the representations is doubly-degenerate then the 3-point function is determined up to an overall constant. We extend our analysis to 4-point functions and find that if two of the W-primary fields are doubly degenerate then the intermediate channels are limited to a finite set and that the corresponding chiral blocks are determined up to an overall constant. This corresponds to the existence of a linear differential equation for the chiral blocks with two completely degenerate fields as has been found in the work of Bajnok~et~al.Comment: 10 pages, LaTeX 2.09, DAMTP-93-4

Carbon-Based Fibrous EDLC Capacitors and Supercapacitors

This paper investigates electrochemical double-layer capacitors (EDLCs) including two alternative types of carbon-based fibrous electrodes, a carbon fibre woven fabric (CWF) and a multiwall carbon nanotube (CNT) electrode, as well as hybrid CWF-CNT electrodes. Two types of separator membranes were also considered. An organic gel electrolyte PEO-LiCIO4-EC-THF was used to maintain a high working voltage. The capacitor cells were tested in cyclic voltammetry, charge-discharge, and impedance tests. The best separator was a glass fibre-fine pore filter. The carbon woven fabric electrode and the corresponding supercapacitor exhibited superior performance per unit area, whereas the multiwall carbon nanotube electrode and corresponding supercapacitor demonstrated excellent specific properties. The hybrid CWF-CNT electrodes did not show a combined improved performance due to the lack of carbon nanotube penetration into the carbon fibre fabric

Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities

Online Social Communities (OSCs) provide a medium for connecting people, sharing news, eliciting information, and finding jobs, among others. The dynamics of the interaction among the members of OSCs is not always growth dynamics. Instead, a $\textit{decay}$ or $\textit{inactivity}$ dynamics often happens, which makes an OSC obsolete. Understanding the behavior and the characteristics of the members of an inactive community help to sustain the growth dynamics of these communities and, possibly, prevents them from being out of service. In this work, we provide two prediction models for predicting the interaction decay of community members, namely: a Simple Threshold Model (STM) and a supervised machine learning classification framework. We conducted evaluation experiments for our prediction models supported by a $\textit{ground truth}$ of decayed communities extracted from the StackExchange platform. The results of the experiments revealed that it is possible, with satisfactory prediction performance in terms of the F1-score and the accuracy, to predict the decay of the activity of the members of these communities using network-based attributes and network-exogenous attributes of the members. The upper bound of the prediction performance of the methods we used is $0.91$ and $0.83$ for the F1-score and the accuracy, respectively. These results indicate that network-based attributes are correlated with the activity of the members and that we can find decay patterns in terms of these attributes. The results also showed that the structure of the decayed communities can be used to support the alive communities by discovering inactive members.Comment: pre-print for the 4th European Network Intelligence Conference - 11-12 September 2017 Duisburg, German

Comparison of radiation damage in silicon induced by proton and neutron irradiation

The subject of radiation damage to Si detectors induced by 24-GeV/c protons and nuclear reactor neutrons has been studied. Detectors fabricated on single-crystal silicon enriched with various impurities have been tested. Significant differences in electrically active defects have been found between the various types of material. The results of the study suggest for the first time that the widely used nonionizing energy loss (NIEL) factors are insufficient for normalization of the electrically active damage in case of oxygen- and carbon-enriched silicon detectors. It has been found that a deliberate introduction of impurities into the semiconductor can affect the radiation hardness of silicon detectors. (16 refs)

Correlations in interacting systems with a network topology

We study pair correlations in cooperative systems placed on complex networks. We show that usually in these systems, the correlations between two interacting objects (e.g., spins), separated by a distance $\ell$, decay, on average, faster than $1/(\ell z_\ell)$. Here $z_\ell$ is the mean number of the $\ell$-th nearest neighbors of a vertex in a network. This behavior, in particular, leads to a dramatic weakening of correlations between second and more distant neighbors on networks with fat-tailed degree distributions, which have a divergent number $z_2$ in the infinite network limit. In this case, only the pair correlations between the nearest neighbors are observable. We obtain the pair correlation function of the Ising model on a complex network and also derive our results in the framework of a phenomenological approach.Comment: 5 page

Cyclotron resonance of the quasi-two-dimensional electron gas at Hg1-xCdxTe grain boundaries

The magnetotransmission of a p-type Hg0.766Cd0.234Te bicrystal containing a single grain boundary with an inversion layer has been investigated in the submillimetre wavelength range. For the first time the cyclotron resonance lines belonging to the various electric subbands of a quasi-two-dimensional carrier system at a grain boundary could be detected. The measured cyclotron masses and the subband densities determined from Shubnikov-de Haas experiments are compared with theoretical predictions and it is found that the data can be explained very well within the framework of a triangular well approximation model which allows for non-parabolic effects

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure