Governing Asylum without ''Being There": Ghost Bureaucracy, Outsourcing, and the Unreachability of the State

When, where, and how do asylum seekers encounter the state? Anyone seeking asylum in the Global North might meet state authorities of the country where they want to apply for international protection long before arriving at its borders. However, if the state often becomes “very present” by transcending its geopolitical margins in border control, once asylum seekers have managed to cross into national territory, the state frequently vanishes. Insufficient information, opaque proceedings, and difficulties in reaching state agencies, which dramatically increased with the COVID pandemic, often translate into a denial of asylum seekers’ rights and their exclusion from welfare programs. Moreover, following a widespread tendency to outsource public services, access to asylum and related welfare programmes are being increasingly mediated by a range of nonstate actors (such as NGOs, activist groups, companies, and individuals) acting as state agents. Drawing on the analysis of ethnographic results from Spain and Italy, this article proposes the concept of “ghost bureaucracy” to theorise the street-level bureaucrats from their absence and explore asylum seekers’ encounters with a seemingly powerful and omnipresent but unreachable state through closed offices, digital bureaucracy and third-party actors

Tunable delay lines in silicon photonics: coupled resonators and photonic crystals, a comparison

In this paper, we report a direct comparison between coupled resonator optical waveguides (CROWs) and photonic crystal waveguides (PhCWs), which have both been exploited as tunable delay lines. The two structures were fabricated on the same silicon-on-insulator (SOI) technological platform, with the same fabrication facilities and evaluated under the same signal bit-rate conditions. We compare the frequency- and time-domain response of the two structures; the physical mechanism underlying the tuning of the delay; the main limits induced by loss, dispersion, and structural disorder; and the impact of CROW and PhCW tunable delay lines on the transmission of data stream intensity and phase modulated up to 100 Gb/s. The main result of this study is that, in the considered domain of applications, CROWs and PhCWs behave much more similarly than one would expect. At data rates around 100 Gb/s, CROWs and PhCWs can be placed in competition. Lower data rates, where longer absolute delays are required and propagation loss becomes a critical issue, are the preferred domain of CROWs fabricated with large ring resonators, while at data rates in the terabit range, PhCWs remain the leading technology

Operations management system

The objective of an operations management system is to provide an orderly and efficient method to operate and maintain aerospace vehicles. Concepts are described for an operations management system and the key technologies are highlighted which will be required if this capability is brought to fruition. Without this automation and decision aiding capability, the growing complexity of avionics will result in an unmanageable workload for the operator, ultimately threatening mission success or survivability of the aircraft or space system. The key technologies include expert system application to operational tasks such as replanning, equipment diagnostics and checkout, global system management, and advanced man machine interfaces. The economical development of operations management systems, which are largely software, will require advancements in other technological areas such as software engineering and computer hardware

How do random Fibonacci sequences grow?

We study two kinds of random Fibonacci sequences defined by $F_1=F_2=1$ and for $n\ge 1$, $F_{n+2} = F_{n+1} \pm F_{n}$ (linear case) or $F_{n+2} = |F_{n+1} \pm F_{n}|$ (non-linear case), where each sign is independent and either + with probability $p$ or - with probability $1-p$ ($0<p\le 1$). Our main result is that the exponential growth of $F_n$ for $0<p\le 1$ (linear case) or for $1/3\le p\le 1$ (non-linear case) is almost surely given by $$\int_0^\infty \log x d\nu_\alpha (x),$$ where $\alpha$ is an explicit function of $p$ depending on the case we consider, and $\nu_\alpha$ is an explicit probability distribution on \RR_+ defined inductively on Stern-Brocot intervals. In the non-linear case, the largest Lyapunov exponent is not an analytic function of $p$, since we prove that it is equal to zero for $0<p\le1/3$. We also give some results about the variations of the largest Lyapunov exponent, and provide a formula for its derivative

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal -transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, Akt-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the Akt-repressed signal as glycogen synthase kinase 3 (GSK3)–catalyzed phosphorylation of Ser37 on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser37 promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Fermions and Loops on Graphs. I. Loop Calculus for Determinant

This paper is the first in the series devoted to evaluation of the partition function in statistical models on graphs with loops in terms of the Berezin/fermion integrals. The paper focuses on a representation of the determinant of a square matrix in terms of a finite series, where each term corresponds to a loop on the graph. The representation is based on a fermion version of the Loop Calculus, previously introduced by the authors for graphical models with finite alphabets. Our construction contains two levels. First, we represent the determinant in terms of an integral over anti-commuting Grassman variables, with some reparametrization/gauge freedom hidden in the formulation. Second, we show that a special choice of the gauge, called BP (Bethe-Peierls or Belief Propagation) gauge, yields the desired loop representation. The set of gauge-fixing BP conditions is equivalent to the Gaussian BP equations, discussed in the past as efficient (linear scaling) heuristics for estimating the covariance of a sparse positive matrix.Comment: 11 pages, 1 figure; misprints correcte

A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

Modelling of photonic wire Bragg Gratings

Some important properties of photonic wire Bragg grating structures have been investigate. The design, obtained as a generalisation of the full-width gap grating, has been modelled using 3D finite-difference time-domain simulations. Different types of stop-band have been observed. The impact of the grating geometry on the lowest order (longest wavelength) stop-band has been investigated - and has identified deeply indented configurations where reduction of the stop-bandwidth and of the reflectivity occurred. Our computational results have been substantially validated by an experimental demonstration of the fundamental stop-band of photonic wire Bragg gratings fabricated on silicon-on-insulator material. The accuracy of two distinct 2D computational models based on the effective index method has also been studied - because of their inherently much greater rapidity and consequent utility for approximate initial designs. A 2D plan-view model has been found to reproduce a large part of the essential features of the spectral response of full 3D models