Computing toric degenerations of flag varieties

We compute toric degenerations arising from the tropicalization of the full flag varieties $\mathcal{F}\ell_4$ and $\mathcal{F}\ell_5$ embedded in a product of Grassmannians. For $\mathcal{F}\ell_4$ and $\mathcal{F}\ell_5$ we compare toric degenerations arising from string polytopes and the FFLV polytope with those obtained from the tropicalization of the flag varieties. We also present a general procedure to find toric degenerations in the cases where the initial ideal arising from a cone of the tropicalization of a variety is not prime.Comment: 35 pages, 6 figure

Relaxation oscillations, stability, and cavity feedback in a superradiant Raman laser

We experimentally study the relaxation oscillations and amplitude stability properties of an optical laser operating deep into the bad-cavity regime using a laser-cooled $^{87}$Rb Raman laser. By combining measurements of the laser light field with nondemolition measurements of the atomic populations, we infer the response of the gain medium represented by a collective atomic Bloch vector. The results are qualitatively explained with a simple model. Measurements and theory are extended to include the effect of intermediate repumping states on the closed-loop stability of the oscillator and the role of cavity feedback on stabilizing or enhancing relaxation oscillations. This experimental study of the stability of an optical laser operating deep into the bad-cavity regime will guide future development of superradiant lasers with ultranarrow linewidths.Comment: 9 pages, 6 figure

The time course of creativity: Multivariate classification of default and executive network contributions to creative cognition over time

Research indicates that creative cognition depends on both associative and controlled processes, corresponding to the brain's default mode network (DMN) and executive control network (ECN) networks. However, outstanding questions include how the DMN and ECN operate over time during creative task performance, and whether creative cognition involves distinct generative and evaluative stages. To address these questions, we used multivariate pattern analysis (MVPA) to assess how the DMN and ECN contribute to creative cognition over three successive time phases during the production of a single creative idea. Training classifiers to predict trial condition (creative vs non-creative), we used classification accuracy as a measure of the extent of creative activity in each brain network and time phase. Across both networks, classification accuracy was highest in early phases, decreased in mid phases, and increased again in later phases, following a U-shaped curve. Notably, classification accuracy was significantly greater in the ECN than the DMN during early phases, while differences between networks at later time phases were non-significant. We also computed correlations between classification accuracy and human-rated creative performance, to assess how relevant the creative activity in each network was to the creative quality of ideas. In line with expectations, classification accuracy in the DMN was most related to creative quality in early phases, decreasing in later phases, while classification accuracy in the ECN was least related to creative quality in early phases, increasing in later phases. Given the theorized roles of the DMN in generation and the ECN in evaluation, we interpret these results as tentative evidence for the existence of separate generative and evaluative stages in creative cognition that depend on distinct neural substrates

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Determination of specific gravity of municipal solid waste

This investigation was conducted to evaluate experimental determination of specific gravity (Gs) of municipal solid waste (MSW). Water pycnometry, typically used for testing soils was adapted for testing MSW using a large flask with 2000 mL capacity and specimens with 100–350 g masses. Tests were conducted on manufactured waste samples prepared using US waste constituent components; fresh wastes obtained prior and subsequent to compaction at an MSW landfill; and wastes obtained from various depths at the same landfill. Factors that influence specific gravity were investigated including waste particle size, compaction, and combined decomposition and stress history. The measured average specific gravities were 1.377 and 1.530 for as-prepared/uncompacted and compacted manufactured wastes, respectively; 1.072 and 1.258 for uncompacted and compacted fresh wastes, respectively; and 2.201 for old wastes. The average organic content and degree of decomposition were 77.2% and 0%, respectively for fresh wastes and 22.8% and 88.3%, respectively for old wastes. The Gs increased with decreasing particle size, compaction, and increasing waste age. For fresh wastes, reductions in particle size and compaction caused occluded intraparticle pores to be exposed and waste particles to be deformed resulting in increases in specific gravity. For old wastes, the high Gs resulted from loss of biodegradable components that have low Gs as well as potential access to previously occluded pores and deformation of particles due to both degradation processes and applied mechanical stresses. The Gs was correlated to the degree of decomposition with a linear relationship. Unlike soils, the Gs for MSW was not unique, but varied in a landfill environment due both to physical/mechanical processes and biochemical processes. Specific gravity testing is recommended to be conducted not only using representative waste composition, but also using representative compaction, stress, and degradation states

Altered sterol metabolism in budding yeast affects mitochondrial iron–sulfur (Fe-S) cluster synthesis

Ergosterol synthesis is essential for cellular growth and viability of the budding yeast Saccharomyces cerevisiae, and intracellular sterol distribution and homeostasis are therefore highly regulated in this species. Erg25 is an iron-containing C4-methyl sterol oxidase that contributes to the conversion of 4,4-dimethylzymosterol to zymosterol, a precursor of ergosterol. The ERG29 gene encodes an endoplasmic reticulum (ER)-associated protein, and here we identified a role for Erg29 in the methyl sterol oxidase step of ergosterol synthesis. ERG29 deletion resulted in lethality in respiring cells, but respiration-incompetent (Rho- or Rho0) cells survived, suggesting that Erg29 loss leads to accumulation of oxidized sterol metabolites that affect cell viability. Down-regulation of ERG29 expression in Δerg29 cells indeed led to accumulation of methyl sterol metabolites, resulting in increased mitochondrial oxidants and a decreased ability of mitochondria to synthesize iron-sulfur (Fe-S) clusters due to reduced levels of Yfh1, the mammalian frataxin homolog, which is involved in mitochondrial iron metabolism. Using a high-copy genomic library, we identified suppressor genes that permitted growth of Δerg29 cells on respiratory substrates, and these included genes encoding the mitochondrial proteins Yfh1, Mmt1, Mmt2, and Pet20, which reversed all phenotypes associated with loss of ERG29 Of note, loss of Erg25 also resulted in accumulation of methyl sterol metabolites and also increased mitochondrial oxidants and degradation of Yfh1. We propose that accumulation of toxic intermediates of the methyl sterol oxidase reaction increases mitochondrial oxidants, which affect Yfh1 protein stability. These results indicate an interaction between sterols generated by ER proteins and mitochondrial iron metabolism

Clinical Significance of Manuka and Medical-Grade Honey for Antibiotic-Resistant Infections: A Systematic Review

Antimicrobial resistance is an ever-increasing global issue that has the potential to overtake cancer as the leading cause of death worldwide by 2050. With the passing of the “golden age” of antibiotic discovery, identifying alternative treatments to commonly used antimicrobials is more important than ever. Honey has been used as a topical wound treatment for millennia and more recently has been formulated into a series of medical-grade honeys for use primarily for wound and burn treatment. In this systematic review, we examined the effectiveness of differing honeys as an antimicrobial treatment against a variety of multidrug-resistant (MDR) bacterial species. We analysed 16 original research articles that included a total of 18 different types of honey against 32 different bacterial species, including numerous MDR strains. We identified that Surgihoney was the most effective honey, displaying minimum inhibitory concentrations as low as 0.1% (w/v); however, all honeys reviewed showed a high efficacy against most bacterial species analysed. Importantly, the MDR status of each bacterial strain had no impact on the susceptibility of the organism to honey. Hence, the use of honey as an antimicrobial therapy should be considered as an alternative approach for the treatment of antibiotic-resistant infections