Long time localization of modified surface quasi-geostrophic equations

We discuss the time evolution of a two-dimensional active scalar flow, which extends some properties valid for a two-dimensional incompressible nonviscous fluid. In particular we study some characteristics of the dynamics when the field is initially concentrated in $N$ small disjoint regions, and we discuss the conservation in time of this localization property. We discuss also how long this localization persists, showing that in some cases this happens for quite long times

TESSERACT:Eliminating Experimental Bias in Malware Classification across Space and Time

Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: "spatial bias" caused by distributions of training and testing data that are not representative of a real-world deployment; and "temporal bias" caused by incorrect time splits of training and testing sets, leading to impossible configurations. We propose a set of space and time constraints for experiment design that eliminates both sources of bias. We introduce a new metric that summarizes the expected robustness of a classifier in a real-world setting, and we present an algorithm to tune its performance. Finally, we demonstrate how this allows us to evaluate mitigation strategies for time decay such as active learning. We have implemented our solutions in TESSERACT, an open source evaluation framework for comparing malware classifiers in a realistic setting. We used TESSERACT to evaluate three Android malware classifiers from the literature on a dataset of 129K applications spanning over three years. Our evaluation confirms that earlier published results are biased, while also revealing counter-intuitive performance and showing that appropriate tuning can lead to significant improvements.Comment: This arXiv version (v4) corresponds to the one published at USENIX Security Symposium 2019, with a fixed typo in Equation (4), which reported an extra normalization factor of (1/N). The results in the paper and the released implementation of the TESSERACT framework remain valid and correct as they rely on Python's numpy implementation of area under the curv

Benchmark on Human Simulation Tools: A Transdisciplinary Approach

Nowadays companies have to face a competitive market that requires small volumes with a high level of customisations. In this context, assembly quality and timeliness is crucial. To guarantee flexibility and personalization, manual operations still have a crucial role for a lot of manufacturing sectors, so that workers' conditions and ergonomics are important factors to achieve a better product quality and overall cost reduction. Ergonomics evaluation in manufacturing is a challenging and expensive activity that requires a transdisciplinary approach, to merge technical and social sciences to finally have a consolidated and reliable evaluation. This paper compared two digital human simulations tools offered by Siemens Tecnomatix: Jack and Process Simulate. They were applied on the same industrial case study, concerning the hood assembly of an agricultural machine, comparing results on ergonomics reports and usage time. Results confirmed the advantage of adopting a digital approach to predict the human effort and ergonomic risk related to a series of tasks. At the same time, they showed the major strengths and weaknesses of the two analysed tools and defined how they can be successfully adopted by companies. The paper finally provided guidelines to drive companies in choosing the best tool according to their needs

Mathematical Model for Determining the Coffee Leaf Area

The present study aimed to establish a mathematical model to estimate, in a simple and precise way, the area of the coffee leaves. What has been observed, in other works, already carried out, are many methods and instruments with the purpose of facilitating the measurement of leaf area and most of them are destructive, laborious and costly methods. For this study, 160 leaves of different dimensions were used to test linear and non-linear mathematical models. The linear model, which uses a correction factor (ACF = 0.644 • LF • CF) presented results with high precision (R² = 0.9898), with variations of -1.28% for larger leaves and 0.32% for smaller leaves, validating the method. Therefore, this model can be safely used to estimate the area of Arabica Catuaí 144 Red coffee leaves or similar

Tardive dyskinesia and DRD2, DRD3, DRD4, 5-HT2A variants in schizophrenia: an association study with repeated assessment.

We performed an association study between four candidate genes, DRD2, DRD3, DRD4 and 5-HT2A for the presence of tardive dyskinesia (TD) on 84 patients with residual schizophrenia. The sample was evaluated again for the presence of TD after an interval of 3 years. The first group did not exhibit TD in either observation ( n =34) while in the second group of patients exhibited TD in at least one of the observations ( n =20+18). The clinical and socio-demographic characteristics were not significantly different between the two groups; the genetic analysis revealed a significant correlation between the C/C genotype of 5-HT2A and TD ( p =0.017). An association trend was observed between the 'short' variant of DRD4 and TD ( p =0.022). We did not observe any significant association for the DRD2 and DRD3 polymorphisms

Transcend:Detecting Concept Drift in Malware Classification Models

Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model’s performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android andWindows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training

Characterization of novel clonal murine endothelial cell lines with an extended life span

Summary: A murine endothelial cell line was recently established from microvessels that had invaded a subcutaneous sponge implant (Dong, Q. G.; Bernasconi, S.; Lostaglio, S., et al. Arterioscl. Thromb. Vasc. Biol. 17:1599-1604; 1997). From these sponge-induced endothelial (SIE) cells, we have isolated two subpopulations endowed with different phenotypic properties. Clone SIE-F consists of large, highly spread cells that have a relatively slow growth rate, form contact-inhibited monolayers, do not grow under anchorage-independent conditions, express elevated levels of thrombospondin-1 (TSP-1) and are not tumorigenic in vivo. In contrast, clone SIE-S2 consists of small, spindle-shaped cells that have a high proliferation rate, do not show contact-inhibition, grow under anchorage-independent conditions, express very low levels of TSP-1 and are tumorigenic in vivo. Both clones express the endothelial markers vascular endothelial-cadherin and vascular intercellular adhesion molecule-1, but do not express CD31 and E-selectin. In addition, SIE-S2 cells, but not SIE-F cells, express the α-smooth muscle actin isoform. SIE-S2 cells, but not SIE-F cells, are able to form branching tubes in fibrin gels. The SIE-F and SIE-S2 clones, which have properties of nontransformed and transformed cells, respectively, should provide useful tools to investigate physiological and pathological processes involving vascular endotheliu

Improving Cognition to Increase Treatment Efficacy in Schizophrenia: Effects of Metabolic Syndrome on Cognitive Remediation's Outcome

Cognitive impairment, typically more severe in treatment resistant patients, is considered a hallmark of schizophrenia and the prime driver of functional disability. Recent evidence suggests that metabolic syndrome may contribute to cognitive deficits in schizophrenia, possibly through shared underlying mechanisms. However, results are still contradictory and no study has so far examined the influence of metabolic syndrome on cognitive outcome after cognitive remediation therapy (CRT). Based on these premises, this study aims to investigate the relationship between metabolic syndrome and cognition, specifically considering cognitive outcome after treatment. Secondary objectives include the analysis of the association between cognitive impairment and psychopathological status and, in a subgroup of patients, the evaluation of the effect of Sterol Regulatory Element Binding Transcription Factor 1 (SREBF-1) rs11868035 genetic polymorphism, previously associated with metabolic alterations, on both cognition and metabolic syndrome. One-hundred seventy-two outpatients with schizophrenia were assessed for metabolic parameters and neurocognitive measures and 138 patients, who completed CRT, were re-evaluated for cognition. A subsample of 51 patients was also genotyped for rs11868035 from peripheral blood sample. Results show a negative impact of metabolic syndrome on executive functions and global cognitive outcome after CRT. Data also revealed a significant effect of SREBF-1 polymorphism, with a higher prevalence of metabolic syndrome and worse processing speed performance among G/G homozygous subjects, compared the A allele carriers. Overall these findings support the hypothesis that metabolic alterations may hamper the capacity to restore cognitive deficits, as well as they highlight the need to further explore possible converging mechanisms underlying both cognitive and metabolic dysfunction. At the clinical level, results point to the importance of a comprehensive assessment including the metabolic status of patients and of individualized strategies addressing metabolic dysfunction in order to potentiate treatment outcome in schizophrenia