Self-Updating Models with Error Remediation

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.Comment: 17 pages, 13 figures, published in the proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II conference in the SPIE Defense + Commercial Sensing, 2020 symposiu

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

ZHOUPI controls embryonic cuticle formation via a signalling pathway involving the subtilisin protease ABNORMAL LEAF-SHAPE1 and the receptor kinases GASSHO1 and GASSHO2

International audienceSeed production in angiosperms requires tight coordination of the development of the embryo and the endosperm. The endosperm-specific transcription factor ZHOUPI has previously been shown to play a key role in this process, by regulating both endosperm breakdown and the formation of the embryonic cuticle. To what extent these processes are functionally linked is, however, unclear. In order to address this issue we have concentrated on the subtilisin-like serine protease encoding gene ABNORMAL LEAF-SHAPE1. Expression of ABNORMAL LEAF-SHAPE1 is endosperm specific, and dramatically decreased in zhoupi mutants. We show that, although ABNORMAL LEAF-SHAPE1 is required for normal embryonic cuticle formation, it plays no role in regulating endosperm breakdown. Furthermore, we show that re-introducing ABNORMAL LEAF-SHAPE1 expression in the endosperm of zhoupi mutants partially rescues embryonic cuticle formation without rescuing their persistent endosperm phenotype. Thus, we conclude that ALE1 can normalize cuticle formation in the absence of endosperm breakdown, and that ZHOUPI thus controls two genetically separable developmental processes. Finally, our genetic study shows that ZHOUPI and ABNORMAL LEAF-SHAPE1 promotes formation of embryonic cuticle via a pathway involving embryonically expressed receptor kinases GASSHO1 and GASSHO2. We therefore provide a molecular framework of inter-tissue communication for embryo-specific cuticle formation during embryogenesis

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

Evaluation of a new short generic measure of health status: howRu

Background Quality of life is paramount for patients and clinicians, but existing measures of health were not developed for routine use. Objectives This paper describes the development and testing of a new generic tool for measuring health related quality of life (HRQoL) with direct comparison to the SF-12 Health Survey. Methods The new tool (howRu) has four items (discomfort, distress, disability and dependence), rated using four levels (none, a little, quite a lot and extreme), providing 256 possible states (44); it has an aggregate scoring scheme with a range from 0 (worst) to 12 (best). Psychometric properties were examined in a telephone survey, which also recorded SF-12. Results The howRu script is shorter than SF-12 (45 words vs 294 words) and has better readability statistics. 2751 subjects, all with long-term conditions (average age 62, female 62.8%), completed the survey; 21.7% were at the ceiling (no reported problems on any dimension); 0.9% at the floor. Inter-item correlations, Cronbach's alpha and principal factor analysis suggest that a single summary score is appropriate. Correlations between the physical and mental components of both howRu and SF-12 were as expected. Across all patients the howRu score was correlated with PCS-12 (r=0.74), MCS-12 (r=0.49) and the sum of PCS- 12 and MCS-12 (r=0.81). Subjects were classified by howRu score, primary condition, the number of conditions suffered, age group, duration of illness and area of residence. Across all six classifications, the correlation of themean howRu score with the mean PCS-12 for each class was r=0.91, with MCS-12, r=0.45 and with the sum of PCS-12 and MCS-12, r=0.97. Conclusions howRu is a new short generic measure of HRQoL, with good psychometric properties. It generates similar aggregate results to SF-12. It could provide a quick and easy way for practitioners to monitor the health of patients with long-term conditions

Twenty year fitness trends in young adults and incidence of prediabetes and diabetes: the CARDIA study

The prospective association between cardiorespiratory fitness (CRF) measured in young adulthood and middle age on development of prediabetes, defined as impaired fasting glucose and/or impaired glucose tolerance, or diabetes by middle age remains unknown. We hypothesised that higher fitness levels would be associated with reduced risk for developing incident prediabetes/diabetes by middle age

Convergent evolution of water conducting cells in Marchantia recruited the ZHOUPI gene promoting cell wall reinforcement and programmed cell death

A key adaptation of plants to life on land is the formation of water conducting cells (WCC) for efficient long-distance water transport. Based on morphological analyses it is thought that WCC have evolved independently on multiple occasions. For example, WCC have been lost in all but a few lineages of bryophytes but strikingly, within the liverworts a derived group, the complex thalloids, has evolved a novel externalised water conducting tissue composed of reinforced, hollow cells termed pegged rhizoids. Here we show that pegged rhizoid differentiation in Marchantia polymorpha is controlled by orthologues of the ZHOUPI and ICE bHLH transcription factors required for endosperm cell death in Arabidopsis seeds. By contrast, pegged rhizoid development was not affected by disruption of MpNAC5, the Marchantia orthologue of the VND genes that control WCC formation in flowering plants. We characterize the rapid, genetically controlled programmed cell death process that pegged rhizoids undergo to terminate cellular differentiation, and identify a corresponding upregulation of conserved putative plant cell death effector genes. Lastly, we show that ectopic expression of MpZOU1 increases production of pegged rhizoids and enhances drought tolerance. Our results support that pegged rhizoids having evolved independently of other WCC. We suggest that elements of the genetic control of developmental cell death are conserved throughout land plants and that the ZHOUPI/ICE regulatory module has been independently recruited to promote cell wall modification and programmed cell death in liverwort rhizoids and in the endosperm of flowering plant seed

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy