Asynchronous Stochastic Variational Inference

Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate O(1/√T) given that the number of slaves is bounded by √T (T is the total number of iterations). The implementation is done in a high-performance computing (HPC) environment using message passing interface (MPI) for python (MPI4py). The extensive empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up

A novel intelligent system for securing cash levels using Markov random fields

Financial support from the Spanish Ministry of Universities "Disruptive group decision making systems in fuzzy context: Applications in smart energy and people analytics" (PID2019-103880RB-I00), and Junta de Andalucia (SEJ340) is gratefully acknowledged.The maintenance of cash levels under certain security thresholds is key for the health of the banking sector. In this paper, the monitoring process of branch network cash levels is performed using a single intelligent system which should provide an alert when there are cash shortages at any point of the network. Such an integral solution would provide a unified insight that guarantees that branches with similar cash features are secured as a whole. That is to say, a triggered alarm at a specific branch would indicate that attention must also be paid to similar (in-cash-feature) branches. The system also incorporates a (complementary) specific treatment for individual branches. The Early Warning System for securing cash levels presented in this paper (cash level EWS) is deliberately free of local demographic specifications, thereby overcoming the current lack of worldwide definitions for local demographics. This aspect would be particularly valuable for banking institutions with branch networks all over the world. A further benefit is the cost reductions that are a result of replacing several approaches with a single global one. Instead of local demographic parameters, a solid theoretical model based on Markov random fields (MRFs) has been developed. The use of MRFs means a reduction in the amount of information required. This would mean a higher processing speed as well as a significant reduction in the amount of storage capacity required. To the best of the author's knowledge, this is the first time that MRFs have been applied to cash monitoring.Spanish Ministry of Universities PID2019-103880RB-I00Junta de Andalucia SEJ34

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

Learning Adaptive Regularization for Image Labeling Using Geometric Assignment

We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth. This is accomplished by a Riemannian gradient flow on the manifold of parameters that determine the regularization properties of the assignment flow. Using the symplectic partitioned Runge--Kutta method for numerical integration, it is shown that deriving the sensitivity conditions of the parameter learning problem and its discretization commute. A convenient property of our approach is that learning is based on exact inference. Carefully designed experiments demonstrate the performance of our approach, the expressiveness of the mathematical model as well as its limitations, from the viewpoint of statistical learning and optimal control

Adaptive Filtering Enhances Information Transmission in Visual Cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio

Selective inhibition of cancer cell self-renewal through a Quisinostat-histone H1.0 axis

Continuous cancer growth is driven by subsets of self-renewing malignant cells. Targeting of uncontrolled self-renewal through inhibition of stem cell-related signaling pathways has proven challenging. Here, we show that cancer cells can be selectively deprived of self-renewal ability by interfering with their epigenetic state. Re-expression of histone H1.0, a tumor-suppressive factor that inhibits cancer cell self-renewal in many cancer types, can be broadly induced by the clinically well-tolerated compound Quisinostat. Through H1.0, Quisinostat inhibits cancer cell self-renewal and halts tumor maintenance without affecting normal stem cell function. Quisinostat also hinders expansion of cells surviving targeted therapy, independently of the cancer types and the resistance mechanism, and inhibits disease relapse in mouse models of lung cancer. Our results identify H1.0 as a major mediator of Quisinostat's antitumor effect and suggest that sequential administration of targeted therapy and Quisinostat may be a broadly applicable strategy to induce a prolonged response in patients

Optimal measurement of visual motion across spatial and temporal scales

Sensory systems use limited resources to mediate the perception of a great variety of objects and events. Here a normative framework is presented for exploring how the problem of efficient allocation of resources can be solved in visual perception. Starting with a basic property of every measurement, captured by Gabor's uncertainty relation about the location and frequency content of signals, prescriptions are developed for optimal allocation of sensors for reliable perception of visual motion. This study reveals that a large-scale characteristic of human vision (the spatiotemporal contrast sensitivity function) is similar to the optimal prescription, and it suggests that some previously puzzling phenomena of visual sensitivity, adaptation, and perceptual organization have simple principled explanations.Comment: 28 pages, 10 figures, 2 appendices; in press in Favorskaya MN and Jain LC (Eds), Computer Vision in Advanced Control Systems using Conventional and Intelligent Paradigms, Intelligent Systems Reference Library, Springer-Verlag, Berli

Bactericidal activity of biosynthesized silver nanoparticles against human pathogenic bacteria

Green synthesis is an attractive and eco-friendly approach to generate potent antibacterial silver nanoparticles (Ag-NPs). Such particles have long been used to fight bacteria and represent a promising tool to overcome the emergence of antibiotic-resistant bacteria. In this study, green synthesis of Ag-NPs was attempted using plant extracts of Aloe vera, Portulaca oleracea and Cynodon dactylon. The identity and size of Ag-NPs was characterized by ultraviolet–visible spectrophotometer and scanning electron microscopy. Monodispersed Ag-NPs were produced with a range of different sizes based on the plant extract used. The bactericidal activity of Ag-NPs against a number of human pathogenic bacteria was determined using the disc diffusion method. The results showed that Gram positive bacteria were more susceptible than Gram negative ones to these antibacterial agents. The minimum inhibitory concentration was determined using the 96-well plate method. Finally, the mechanism by which Ag-NPs affect bacteria was investigated by SEM analysis. Bacteria treated with Ag-NPs were seen to undergo shrinkage and to lose their viability. This study provides evidence for a cheap and effective method for synthesizing potent bactericidal Ag-NPs and demonstrates their effectiveness against human pathogenic bacteria

Role of deep sponge grounds in the Mediterranean Sea: a case study in southern Italy

The Mediterranean spongofauna is relatively well-known for habitats shallower than 100 m, but, differently from oceanic basins, information upon diversity and functional role of sponge grounds inhabiting deep environments is much more fragmentary. Aims of this article are to characterize through ROV image analysis the population structure of the sponge assemblages found in two deep habitats of the Mediterranean Sea and to test their structuring role, mainly focusing on the demosponges Pachastrella monilifera Schmidt, 1868 and Poecillastra compressa (Bowerbank, 1866). In both study sites, the two target sponge species constitute a mixed assemblage. In the Amendolara Bank (Ionian Sea), where P. compressa is the most abundant species, sponges extend on a peculiar tabular bedrock between 120 and 180 m depth with an average total abundance of 7.3 +/- 1.1 specimens m(-2) (approximately 230 gWW m(-2) of biomass). In contrast, the deeper assemblage of Bari Canyon (average total abundance 10.0 +/- 0.7 specimens m(-2), approximately 315 gWW m(-2) of biomass), located in the southwestern Adriatic Sea between 380 and 500 m depth, is dominated by P. monilifera mixed with living colonies of the scleractinian Madrepora oculata Linnaeus, 1758, the latter showing a total biomass comparable to that of sponges (386 gWW m(-2)). Due to their erect growth habit, these sponges contribute to create complex three-dimensional habitats in otherwise homogenous environments exposed to high sedimentation rates and attract numerous species of mobile invertebrates (mainly echinoderms) and fish. Sponges themselves may represent a secondary substrate for a specialized associated fauna, such zoanthids. As demonstrated in oceanic environments sponge beds support also in the Mediterranean Sea locally rich biodiversity levels. Sponges emerge also as important elements of benthic-pelagic coupling in these deep habitats. In fact, while exploiting the suspended organic matter, about 20% of the Bari sponge assemblage is also severely affected by cidarid sea urchin grazing, responsible to cause visible damages to the sponge tissues (an average of 12.1 +/- 1.8 gWW of individual biomass removed by grazing). Hence, in deep-sea ecosystems, not only the coral habitats, but also the grounds of massive sponges represent important biodiversity reservoirs and contribute to the trophic recycling of organic matter

Telomerase activity in melanoma and non-melanoma skin cancer

Telomeres are specialized structures consisting of repeat arrays of TTAGGGn located at the ends of chromosomes. They are essential for chromosome stability and, in the majority of normal somatic cells, telomeres shorten with each cell division. Most immortalized cell lines and tumours reactivate telomerase to stabilize the shortening chromosomes. Telomerase activation is regarded as a central step in carcinogenesis and, here, we demonstrate telomerase activation in premalignant skin lesions and also in all forms of skin cancer. Telomerase activation in normal skin was a rare event, and among 16 samples of normal skin (one with a history of chronic sun exposure) 12.5% (2 out of 16) exhibited telomerase activity. One out of 16 (6.25%) benign proliferative lesions, including viral and seborrhoeic wart samples, had telomerase activity. In premalignant actinic keratoses and Bowen's disease, 42% (11 out of 26) of samples exhibited telomerase activity. In the basal cell carcinoma and cutaneous malignant melanoma (CMM) lesions, telomerase was activated in 77% (10 out of 13) and 69% (22 out of 32) respectively. However, only 25% (3 out of 12) of squamous cell carcinomas (SCC) had telomerase activity. With the exception of one SCC sample, telomerase activity in a positive control cell line derived from a fibrosarcoma (HT1080) was not inhibited when mixed with the telomerase-negative SCC or CMM extracts, indicating that, overall, Taq polymerase and telomerase inhibitors were not responsible for the negative results. Mean telomere hybridizing restriction fragment (TRF) analysis was performed in a number of telomerase-positive and -negative samples and, although a broad range of TRF sizes ranging from 3.6 to 17 kb was observed, a relationship between telomerase status and TRF size was not found