Scaling laws for random walks in long-range correlated disordered media

We study the scaling laws of diffusion in two-dimensional media with long-range correlated disorder through exact enumeration of random walks. The disordered medium is modelled by percolation clusters with correlations decaying with the distance as a power law, $r^{-a}$, generated with the improved Fourier filtering method. To characterize this type of disorder, we determine the percolation threshold $p_{\text c}$ by investigating cluster-wrapping probabilities. At $p_{\text c}$, we estimate the (sub-diffusive) walk dimension $d_{\text w}$ for different correlation exponents $a$. Above $p_{\text c}$, our results suggest a normal random walk behavior for weak correlations, whereas anomalous diffusion cannot be ruled out in the strongly correlated case, i.e., for small $a$.Comment: 11 pages, 6 figure

Percolation thresholds and fractal dimensions for square and cubic lattices with long-range correlated defects

We study long-range power-law correlated disorder on square and cubic lattices. In particular, we present high-precision results for the percolation thresholds and the fractal dimension of the largest clusters as function of the correlation strength. The correlations are generated using a discrete version of the Fourier filtering method. We consider two different metrics to set the length scales over which the correlations decay, showing that the percolation thresholds are highly sensitive to such system details. By contrast, we verify that the fractal dimension $d_{\rm f}$ is a universal quantity and unaffected by the choice of metric. We also show that for weak correlations, its value coincides with that for the uncorrelated system. In two dimensions we observe a clear increase of the fractal dimension with increasing correlation strength, approaching $d_{\rm f}\rightarrow 2$. The onset of this change does not seem to be determined by the extended Harris criterion.Comment: 12 pages, 8 figure

Transition from regular to complex behaviour in a discrete deterministic asymmetric neural network model

We study the long time behaviour of the transient before the collapse on the periodic attractors of a discrete deterministic asymmetric neural networks model. The system has a finite number of possible states so it is not possible to use the term chaos in the usual sense of sensitive dependence on the initial condition. Nevertheless, at varying the asymmetry parameter, $k$, one observes a transition from ordered motion (i.e. short transients and short periods on the attractors) to a ``complex'' temporal behaviour. This transition takes place for the same value $k_{\rm c}$ at which one has a change for the mean transient length from a power law in the size of the system ($N$) to an exponential law in $N$. The ``complex'' behaviour during the transient shows strong analogies with the chaotic behaviour: decay of temporal correlations, positive Shannon entropy, non-constant Renyi entropies of different orders. Moreover the transition is very similar to that one for the intermittent transition in chaotic systems: scaling law for the Shannon entropy and strong fluctuations of the ``effective Shannon entropy'' along the transient, for $k > k_{\rm c}$.Comment: 18 pages + 6 figures, TeX dialect: Plain TeX + IOP macros (included

Honeypots and honeynets: issues of privacy

Honeypots and honeynets are popular tools in the area of network security and network forensics. The deployment and usage of these tools are influenced by a number of technical and legal issues, which need to be carefully considered. In this paper, we outline the privacy issues of honeypots and honeynets with respect to their technical aspects. The paper discusses the legal framework of privacy and legal grounds to data processing. We also discuss the IP address, because by EU law, it is considered personal data. The analysis of legal issues is based on EU law and is supported by discussions on privacy and related issues

Relaxation, closing probabilities and transition from oscillatory to chaotic attractors in asymmetric neural networks

Attractors in asymmetric neural networks with deterministic parallel dynamics were shown to present a "chaotic" regime at symmetry eta < 0.5, where the average length of the cycles increases exponentially with system size, and an oscillatory regime at high symmetry, where the typical length of the cycles is 2. We show, both with analytic arguments and numerically, that there is a sharp transition, at a critical symmetry \e_c=0.33, between a phase where the typical cycles have length 2 and basins of attraction of vanishing weight and a phase where the typical cycles are exponentially long with system size, and the weights of their attraction basins are distributed as in a Random Map with reversal symmetry. The time-scale after which cycles are reached grows exponentially with system size $N$, and the exponent vanishes in the symmetric limit, where $T\propto N^{2/3}$. The transition can be related to the dynamics of the infinite system (where cycles are never reached), using the closing probabilities as a tool. We also study the relaxation of the function $E(t)=-1/N\sum_i |h_i(t)|$, where $h_i$ is the local field experienced by the neuron $i$. In the symmetric system, it plays the role of a Ljapunov function which drives the system towards its minima through steepest descent. This interpretation survives, even if only on the average, also for small asymmetry. This acts like an effective temperature: the larger is the asymmetry, the faster is the relaxation of $E$, and the higher is the asymptotic value reached. $E$ reachs very deep minima in the fixed points of the dynamics, which are reached with vanishing probability, and attains a larger value on the typical attractors, which are cycles of length 2.Comment: 24 pages, 9 figures, accepted on Journal of Physics A: Math. Ge

Farsighted Risk Mitigation of Lateral Movement Using Dynamic Cognitive Honeypots

Lateral movement of advanced persistent threats has posed a severe security challenge. Due to the stealthy and persistent nature of the lateral movement, defenders need to consider time and spatial locations holistically to discover latent attack paths across a large time-scale and achieve long-term security for the target assets. In this work, we propose a time-expanded random network to model the stochastic service links in the user-host enterprise network and the adversarial lateral movement. We design cognitive honeypots at idle production nodes and disguise honey links as service links to detect and deter the adversarial lateral movement. The location of the honeypot changes randomly at different times and increases the honeypots' stealthiness. Since the defender does not know whether, when, and where the initial intrusion and the lateral movement occur, the honeypot policy aims to reduce the target assets' Long-Term Vulnerability (LTV) for proactive and persistent protection. We further characterize three tradeoffs, i.e., the probability of interference, the stealthiness level, and the roaming cost. To counter the curse of multiple attack paths, we propose an iterative algorithm and approximate the LTV with the union bound for computationally efficient deployment of cognitive honeypots. The results of the vulnerability analysis illustrate the bounds, trends, and a residue of LTV when the adversarial lateral movement has infinite duration. Besides honeypot policies, we obtain a critical threshold of compromisability to guide the design and modification of the current system parameters for a higher level of long-term security. We show that the target node can achieve zero vulnerability under infinite stages of lateral movement if the probability of movement deterrence is not less than the threshold

Exploring the IL-21–STAT3 Axis as Therapeutic Target for Sézary Syndrome

Sézary syndrome is an aggressive cutaneous T-cell lymphoma. The malignant cells (Sézary cells) are present in skin, lymph nodes, and blood, and express constitutively activated signal transducer and activator of transcription (STAT)3. STAT3 can be activated by IL-21 in vitro and the IL-21 gene itself is a STAT3 target gene, thereby creating an autocrine positive feedback loop that might serve as a therapeutic target. Sézary cells underwent apoptosis when incubated with Stattic, a selective STAT3 inhibitor. STAT3 activation in Sézary cells did not affect expression of the supposed anti-apoptotic STAT3 target genes BCL2, BCL-xL, and SURVIVIN, whereas expression of (proto)oncogenes miR-21, TWIST1, MYC, and PIM1 was significantly increased. CD3/CD28-mediated activation of Sézary cells induced IL-21 expression, accompanied by STAT3 activation and increased proliferation. Blocking IL-21 in CD3/CD28-activated cells had no effects, whereas Stattic abrogated IL-21 expression and cell proliferation. Thus, specific inhibition of STAT3 is highly efficient in the induction of apoptosis of Sézary cells, likely mediated via the regulation of (proto)oncogenes. In contrast, blocking IL-21 alone seems insufficient to affect STAT3 activation, cell proliferation, or apoptosis. These data provide further insights into the pathogenic role of STAT3 in Sézary syndrome and strengthen the notion that STAT3 represents a promising therapeutic target in this disease

Historical sampling reveals dramatic demographic changes in western gorilla populations

Background: Today many large mammals live in small, fragmented populations, but it is often unclear whether this subdivision is the result of long-term or recent events. Demographic modeling using genetic data can estimate changes in long-term population sizes while temporal sampling provides a way to compare genetic variation present today with that sampled in the past. In order to better understand the dynamics associated with the divergences of great ape populations, these analytical approaches were applied to western gorillas (Gorilla gorilla) and in particular to the isolated and Critically Endangered Cross River gorilla subspecies (G. g. diehli).Results: We used microsatellite genotypes from museum specimens and contemporary samples of Cross River gorillas to infer both the long-term and recent population history. We find that Cross River gorillas diverged from the ancestral western gorilla population ~17,800 years ago (95% HDI: 760, 63,245 years). However, gene flow ceased only ~420 years ago (95% HDI: 200, 16,256 years), followed by a bottleneck beginning ~320 years ago (95% HDI: 200, 2,825 years) that caused a 60-fold decrease in the effective population size of Cross River gorillas. Direct comparison of heterozygosity estimates from museum and contemporary samples suggests a loss of genetic variation over the last 100 years.Conclusions: The composite history of western gorillas could plausibly be explained by climatic oscillations inducing environmental changes in western equatorial Africa that would have allowed gorilla populations to expand over time but ultimately isolate the Cross River gorillas, which thereafter exhibited a dramatic population size reduction. The recent decrease in the Cross River population is accordingly most likely attributable to increasing anthropogenic pressure over the last several hundred years. Isolation of diverging populations with prolonged concomitant gene flow, but not secondary admixture, appears to be a typical characteristic of the population histories of African great apes, including gorillas, chimpanzees and bonobos

Micro-computed tomography and histology to explore internal morphology in decapod larvae

Traditionally, the internal morphology of crustacean larvae has been studied using destructive techniques such as dissection and microscopy. The present study combines advances in microcomputed tomography (micro-CT) and histology to study the internal morphology of decapod larvae, using the common spider crab (Maja brachydactyla Balss, 1922) as a model and resolving the individual limitations of these techniques. The synergy of micro-CT and histology allows the organs to be easily identified, revealing simultaneously the gross morphology (shape, size, and location) and histological organization (tissue arrangement and cell identification). Micro-CT shows mainly the exoskeleton, musculature, digestive and nervous systems, and secondarily the circulatory and respiratory systems, while histology distinguishes several cell types and confirms the organ identity. Micro-CT resolves a discrepancy in the literature regarding the nervous system of crab larvae. The major changes occur in the metamorphosis to the megalopa stage, specifically the formation of the gastric mill, the shortening of the abdominal nerve cord, the curving of the abdomen beneath the cephalothorax, and the development of functional pereiopods, pleopods, and lamellate gills. The combination of micro-CT and histology provides better results than either one alone.Financial support was provided by the Spanish Ministry of Economy and Competitiveness through the INIA project (grant number RTA2011-00004-00-00) to G.G. and a pre-doctoral fellowship to D.C. (FPI-INIA)