A bio-inspired image coder with temporal scalability

We present a novel bio-inspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with time-delays. The second stage is a time-dependent analog-to-digital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS 2011

A novel image compression algorithm for high resolution 3D reconstruction

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Molecular basis of association of receptor activity-modifying protein 3 with the family B G protein-coupled secretin receptor

The three receptor activity-modifying proteins (RAMPs) have been recognized as being important for the trafficking and function of a subset of family B G protein-coupled receptors, although the structural basis for this has not been well established. In the current work, we use morphological fluorescence techniques, bioluminescence resonance energy transfer, and bimolecular fluorescence complementation to demonstrate that the secretin receptor associates specifically with RAMP3, but not with RAMP1 or RAMP2. We use truncation constructs, peptide competition experiments, and chimeric secretin-GLP1 receptor constructs to establish that this association is structurally specific, dependent on the intramembranous region of the RAMP and TM6 and TM7 of this receptor. There were no observed changes in secretin-stimulated cAMP, intracellular calcium, ERK1/2 phosphorylation, or receptor internalization in receptor-bearing COS or CHO-K1 cells in the presence or absence of exogenous RAMP transfection, although the secretin receptor trafficks normally to the cell surface in these cells in a RAMP-independent manner, resulting in both free and RAMP-associated receptor on the cell surface. RAMP3 association with this receptor was shown to be capable of rescuing a receptor mutant (G241C) that is normally trapped intracellularly in the biosynthetic machinery. Similarly, secretin receptor expression had functional effects on adrenomedullin activity, with increasing secretin receptor expression competing for RAMP3 association with the calcitonin receptor-like receptor to yield a functional adrenomedullin receptor. These data provide important new insights into the structural basis for RAMP3 interaction with a family B G protein-coupled receptor, potentially providing a highly selective target for drug action. This may be representative of similar interactions between other members of this receptor family and RAMP proteins

New Generation Cooperative and Cognitive Dual Satellite Systems: Performance Evaluation

Investigating innovative satellite architectures with enhanced system through- put is one of the most important challenges towards realizing the next generation of satellite communication systems. In this context, we study two advanced architectures, namely cooperative and cognitive satellite systems. These designs allow the spectral coexistence of two multibeam satellites over a common coverage area with the overlapping beam patterns. In the cooperative dual satellite system, we consider coordination between two coexisting transmitters in order to reduce the intersatellite interference. This is achieved by employing adequate user scheduling, based on the channel state information of each user. To this end, a semi-orthogonal interference aware scheduling algorithm is applied. Further, in the cognitive dual satellite system, we employ a cognitive beamhopping technique assuming that the secondary gateway is aware of the primary's beamhopping pattern. Moreover, we compare the performances of these schemes with those of the conventional multi- beam and overlapping dual satellite systems in terms of spectral efficiency, power efficiency and user fairness. Finally, we provide several insights on the performance of these schemes and provide interesting future works in these domains

Heavy Alcohol Use Is Associated With Worse Retention in HIV Care

Poor retention in HIV care is associated with worse clinical outcomes and increased HIV transmission. We examined the relationship between self-reported alcohol use, a potentially modifiable behavior, and retention

Depressive Symptoms and Engagement in Human Immunodeficiency Virus Care Following Antiretroviral Therapy Initiation

The effect of depressive symptoms on progression through the human immunodeficiency virus (HIV) treatment cascade is poorly characterized. Methods. We included participants from the Centers for AIDS Research Network of Integrated Clinic Systems cohort who were antiretroviral therapy (ART) naive, had at least 1 viral load and HIV appointment measure after ART initiation, and a depressive symptom measure within 6 months of ART initiation. Recent depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) and categorized using a validated cut point (PHQ-9 =10). We followed participants from ART initiation through the first of the following events: loss to follow-up (12 months with no HIV appointment), death, administrative censoring (2011-2014), or 5 years of follow-up. We used log binomial models with generalized estimating equations to estimate associations between recent depressive symptoms and having a detectable viral load (=75 copies/mL) or missing an HIV visit over time. Results. We included 1057 HIV-infected adults who contributed 2424 person-years. At ART initiation, 30% of participants reported depressive symptoms. In multivariable analysis, recent depressive symptoms increased the risk of having a detectable viral load (risk ratio [RR], 1.28; 95% confidence interval [CI], 1.07, 1.53) over time. The association between depressive symptoms and missing an HIV visit (RR, 1.20; 95% CI, 1.05, 1.36) moved to the null after adjustment for preexisting mental health conditions (RR, 1.00; 95% CI, 0.85, 1.18). Conclusions. Recent depressive symptoms are a risk factor for unsuppressed viral load, while preexisting mental health conditions may influence HIV appointment adherence