Unsupervised Learning with Self-Organizing Spiking Neural Networks

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks

A novel KIF11 mutation in a Turkish patient with microcephaly, lymphedema, and chorioretinal dysplasia from a consanguineous family.

Microcephaly–lymphedema–chorioretinal dysplasia (MLCRD) syndrome is a rare syndrome that was first described in 1992. Characteristic craniofacial features include severe microcephaly, upslanting palpebral fissures, prominent ears, a broad nose, and a long philtrum with a pointed chin. Recently, mutations in KIF11 have been demonstrated to cause dominantly inherited MLCRD syndrome. Herein, we present a patient with MLCRD syndrome whose parents were first cousins. The parents are unaffected, and thus a recessive mode of inheritance for the disorder was considered likely. However, the propositus carries a novel, de novo nonsense mutationinexon2 of KIF11. The patient also had midline cleft tongue which has not previously been described in this syndrome

Laboratory evaluation of diatomaceous earth against main stored product insects

The sensitivity of the main external and internal stored product insect pests to the commercial formulation of Detia Degesch Diatomaceous Earth – DDDE - Inerto (DE) was studied in laboratory experiments. The tested insects were adults of internal feeders Sitophilus oryzae Rhyzopertha dominica and external feeders Oryzaephilus surinamensis, Tribolium castaneum, and larvae (third instar) of T.castaneum. The DE was applied to wheat grain of 12% moisture content at concentrations of 0.5, 1.0, 2.0 and 4.0 g/kg of grain. The treated and untreated (control) grain were kept at 28°C and 65 ± 5% r.h. The numbers of dead and survived insects were counted two, three and four weeks after treatment. The number of adult progeny was counted nine weeks after treatment. At a concentration of 0.5 g/kg, mortality of S. oryzae and O. surinamensis after three weeks of exposure to DE were 92 and 86%, respectively. In contrast, mortality of T. castaneum and R. dominica adults was 3 and 37%, respectively. Progeny production of O. surinamensis and T. castaneum at a concentration of 2 g/kg was negligible, since only few individuals were recorded nine weeks after treatment, in comparison with the high progeny production in the control grain. The progeny of S. oryzae was also reduced. In contrast, for R. dominica was reduced only twice, in comparison with the control. In the case of T. castaneum larvae, at a concentration of 2 g/kg, after 4 weeks of exposure, 37% of the larvae emerged to adults, compared with 95% in control. Nine weeks after treatment, the number of F1adults was 100% suppressed. DE efficacy was similar at 4 g/kg. Based on the findings of the present study, the efficacy of the tested DE was influenced by DE concentration, insect species, developmental stage and exposure interval to the treated commodity.Keywords: Diatomaceous earth, Stored product insects, Wheat grai

Improvement of phosphine fumigation by the use of Speedbox

Today, phosphine is turning to be a major fumigant for controlling insects in stored products. However, few limitations, such as low temperatures and relatively long exposure time, limit the phosphine use. In order to improve phosphine application, a special devise, containing a heater and a ventilator, called "Speedbox" has been developed by Detia Degesch GmbH Germany. For studying the effectiveness of phosphine fumigation using Speedbox, we have conducted two kinds of experiments: one in a fumigation room (Pilot) and other in commercial warehouse. For pilot fumigation, adults, pupae and late larvae of Sitophilus oryzae, Rhyzopertha dominica, Oryzaephilus surinamensis, Trogoderma granarium and Callosobruchus maculatus, and all stages of Tribolium castaneum Herbst, Plodia interpunctella and Ephestia cautella were used as test insects. One to three Degesch Plates (about 2-6 g of phosphine gas per m3) were used. Exposure time was 1 to 3 days. The phosphine concentrtion was monitored by Bedfont device model 415. At 4 g/m3 for 48 ha maximum of phosphine concentration of 1460 ppm was reached. The total mortality of all tested insects and stages was recorded, except the eggs of E. cautella (98%). The commercial stack fumigation was done at the dosages of 2-4 g/m3, exposure time of 2-4 days and commodity temperatures of 6-17ºC. At a target concentration of 4 g/m3, 2 hours after beginning of the treatment, the concentration of the gas has reached 414 ppm, with a maximum of 1480 ppm. The total mortality of tested insects at adult, late larvae and pupae stages was recorded. The use of Speedbox allows one-day decrease in the plates degassing time, recirculation of the gas and its event distribution in the treated space and controlling major stored product insects for shorter exposure time at low temperatures. Keywords: Fumigation; Posphine; Speedbox; Stored-product insect

A Deep Dive into Adversarial Robustness in Zero-Shot Learning

Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluating the adversarial robustness of ZSL and GZSL models. We leverage the well-established label embedding model and subject it to a set of established adversarial attacks and defenses across multiple datasets. In addition to creating possibly the first benchmark on adversarial robustness of ZSL models, we also present analyses on important points that require attention for better interpretation of ZSL robustness results. We hope these points, along with the benchmark, will help researchers establish a better understanding what challenges lie ahead and help guide their work.Comment: To appear in ECCV 2020, Workshop on Adversarial Robustness in the Real Worl

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of the Schelling model for agents described simply by a continuously distributed variable - wealth. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model where segregation obtains between two races, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move - that is, with some probability, if agents are allowed to move even though the threshold level condition is contravened - we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution. We investigate the nature of this sharp transformation between segregated and mixed states, and find that it is because of a non-linear relationship between allowed moves and disallowed moves. For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves, but this tapers off as the fraction of disallowed moves increase further and finally settles at a stable value, remaining invariant to any further increase in disallowed moves. It is the overall effect of the dynamics in the initial region (with small numbers of disallowed moves) that shifts the system away from a state of segregation rapidly to a mixed wealth state. The contravention of the tolerance condition could be interpreted as public policy interventions like minimal levels of social housing or housing benefit transfers to poorer households. Our finding therefore suggests that it might require only very limited levels of such public intervention - just sufficient to enable a small fraction of disallowed moves, because the dynamics generated by such moves could spur the transformation from a segregated to mixed equilibrium.Comment: 12 pages, 7 figure

Integrating Learning and Reasoning with Deep Logic Models

Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning

Near Real Time Data Processing In ICOS RI

This paper describes the implementation of (near) real-time (NRT) data processing in the recently launched European environmental research infrastructure ICOS. NRT applications include handling of raw sensor data (including safe storage and quality control), processing and evaluation of greenhouse gas mixing ratios and exchange fluxes, and the provision of data to the RI’s user communities