The Way Forward on Counter-Terrorism: Global Perspectives

There have been thousands of public conferences and closed-door meetings on terrorism and counter-terrorism since 11 September 2001. They usually end up with recommendations and then everybody goes home after the group photo has been taken. This article will deal with the following questions: what happened to all these recommendations? Who has acted upon them and actually implemented them? Who has evaluated them? Were they any good? Specifically, it will analyze five critical issues: (i) the definition problem; (ii) the communication problem; (iii) the political problem; (iv) the religious problem; (v) the radicalization problem. Finally, it will be provided twelve rules for preventing and combating terrorism

Selected literature on radicalization and de-radicalization of terrorists: monographs, edited volumes, grey literature and prime articles published since the 1960s

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Understanding the Roots of Radicalisation on Twitter

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation

Code wars: steganography, signals intelligence, and terrorism

This paper describes and discusses the process of secret communication known as steganography. The argument advanced here is that terrorists are unlikely to be employing digital steganography to facilitate secret intra-group communication as has been claimed. This is because terrorist use of digital steganography is both technically and operationally implausible. The position adopted in this paper is that terrorists are likely to employ low-tech steganography such as semagrams and null ciphers instead

Quasiparticle Lifetime in a Finite System: A Non--Perturbative Approach

The problem of electron--electron lifetime in a quantum dot is studied beyond perturbation theory by mapping it onto the problem of localization in the Fock space. We identify two regimes, localized and delocalized, corresponding to quasiparticle spectral peaks of zero and finite width, respectively. In the localized regime, quasiparticle states are very close to single particle excitations. In the delocalized state, each eigenstate is a superposition of states with very different quasiparticle content. A transition between the two regimes occurs at the energy $\simeq\Delta(g/\ln g)^{1/2}$, where $\Delta$ is the one particle level spacing, and $g$ is the dimensionless conductance. Near this energy there is a broad critical region in which the states are multifractal, and are not described by the Golden Rule.Comment: 13 pages, LaTeX, one figur

Effects of anharmonic strain on phase stability of epitaxial films and superlattices: applications to noble metals

Epitaxial strain energies of epitaxial films and bulk superlattices are studied via first-principles total energy calculations using the local-density approximation. Anharmonic effects due to large lattice mismatch, beyond the reach of the harmonic elasticity theory, are found to be very important in Cu/Au (lattice mismatch 12%), Cu/Ag (12%) and Ni/Au (15%). We find that is the elastically soft direction for biaxial expansion of Cu and Ni, but it is for large biaxial compression of Cu, Ag, and Au. The stability of superlattices is discussed in terms of the coherency strain and interfacial energies. We find that in phase-separating systems such as Cu-Ag the superlattice formation energies decrease with superlattice period, and the interfacial energy is positive. Superlattices are formed easiest on (001) and hardest on (111) substrates. For ordering systems, such as Cu-Au and Ag-Au, the formation energy of superlattices increases with period, and interfacial energies are negative. These superlattices are formed easiest on (001) or (110) and hardest on (111) substrates. For Ni-Au we find a hybrid behavior: superlattices along and like in phase-separating systems, while for they behave like in ordering systems. Finally, recent experimental results on epitaxial stabilization of disordered Ni-Au and Cu-Ag alloys, immiscible in the bulk form, are explained in terms of destabilization of the phase separated state due to lattice mismatch between the substrate and constituents.Comment: RevTeX galley format, 16 pages, includes 9 EPS figures, to appear in Physical Review

Early Change in Urine Protein as a Surrogate End Point in Studies of IgA Nephropathy: An Individual-Patient Meta-analysis

Background The role of change in proteinuria as a surrogate end point for randomized trials in immunoglobulin A nephropathy (IgAN) has previously not been thoroughly evaluated. Study Design Individual patient–level meta-analysis. Setting & Population Individual-patient data for 830 patients from 11 randomized trials evaluating 4 intervention types (renin-angiotensin system [RAS] blockade, fish oil, immunosuppression, and steroids) examining associations between changes in urine protein and clinical end points at the individual and trial levels. Selection Criteria for Studies Randomized controlled trials of IgAN with measurements of proteinuria at baseline and a median of 9 (range, 5-12) months follow-up, with at least 1 further year of follow-up for the clinical outcome. Predictor 9-month change in proteinuria. Outcome Doubling of serum creatinine level, end-stage renal disease, or death. Results Early decline in proteinuria at 9 months was associated with lower risk for the clinical outcome (HR per 50% reduction in proteinuria, 0.40; 95% CI, 0.32-0.48) and was consistent across studies. Proportions of treatment effect on the clinical outcome explained by early decline in proteinuria were estimated at 11% (95% CI, −19% to 41%) for RAS blockade and 29% (95% CI, 6% to 53%) for steroid therapy. The direction of the pooled treatment effect on early change in proteinuria was in accord with the direction of the treatment effect on the clinical outcome for steroids and RAS blockade. Trial-level analyses estimated that the slope for the regression line for the association of treatment effects on the clinical end points and for the treatment effect on proteinuria was 2.15 (95% Bayesian credible interval, 0.10-4.32). Limitations Study population restricted to 11 trials, all having fewer than 200 patients each with a limited number of clinical events. Conclusions Results of this analysis offer novel evidence supporting the use of an early reduction in proteinuria as a surrogate end point for clinical end points in IgAN in selected settings

Carrots, Sticks, and Insurgent Targeting of Civilians

How do conciliatory and coercive counterinsurgency tactics affect militant group violence against civilians? Scholars of civil war increasingly seek to understand intentional civilian targeting, often referred to as terrorism. Extant research emphasizes group weakness, or general state attributes such as regime type. We focus on terrorism as violent communication and as a response to government actions. State tactics toward groups, carrots and sticks, should be important for explaining insurgent terror. We test the argument using new data on terrorism by insurgent groups, with many time-varying variables, covering 1998 through 2012. Results suggest government coercion against a group is associated with subsequent terrorism by that group. However, this is only the case for larger insurgent groups, which raises questions about the notion of terrorism as a weapon of the weak. Carrots are often negatively related to group terrorism. Other factors associated with insurgent terrorism include holding territory, ethnic motivation, and social service provision

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/