Road Context-aware Intrusion Detection System for Autonomous Cars

Security is of primary importance to vehicles. The viability of performing remote intrusions onto the in-vehicle network has been manifested. In regard to unmanned autonomous cars, limited work has been done to detect intrusions for them while existing intrusion detection systems (IDSs) embrace limitations against strong adversaries. In this paper, we consider the very nature of autonomous car and leverage the road context to build a novel IDS, named Road context-aware IDS (RAIDS). When a computer-controlled car is driving through continuous roads, road contexts and genuine frames transmitted on the car's in-vehicle network should resemble a regular and intelligible pattern. RAIDS hence employs a lightweight machine learning model to extract road contexts from sensory information (e.g., camera images and distance sensor values) that are used to generate control signals for maneuvering the car. With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network. Anomalous frames that substantially deviate from road context will be discerned as intrusions. We have implemented a prototype of RAIDS with neural networks, and conducted experiments on a Raspberry Pi with extensive datasets and meaningful intrusion cases. Evaluations show that RAIDS significantly outperforms state-of-the-art IDS without using road context by up to 99.9% accuracy and short response time.Comment: This manuscript presents an intrusion detection system that makes use of road context for autonomous car

Learning in anticipation of reward and punishment: perspectives across the human lifespan

Learning to act to receive reward and to withhold to avoid punishment has been found to be easier than learning the opposite contingencies in young adults. To what extent this type of behavioral adaptation might develop during childhood and adolescence and differ during aging remains unclear. We therefore tested 247 healthy individuals across the human life span (7-80 years) with an orthogonalized valenced go/no-go learning task. Computational modeling revealed that peak performance in young adults was attributable to greater sensitivity to both reward and punishment. However, in children and adolescents, we observed an increased bias toward action but not reward sensitivity. By contrast, reduced learning in midlife and older adults was accompanied by decreased reward sensitivity and especially punishment sensitivity along with an age-related increase in the Pavlovian bias. These findings reveal distinct motivation-dependent learning capabilities across the human life span, which cannot be probed using conventional go/reward no-go/punishment style paradigms that have important implications in lifelong education

Bovine Herpesvirus Type 1 (BHV-1) UL49.5 Luminal Domain Residues 30 to 32 Are Critical for MHC-I Down-Regulation in Virus-Infected Cells

Bovine herpesvirus type 1 (BHV-1) UL49.5 inhibits transporter associated with antigen processing (TAP) and down-regulates cell-surface expression of major histocompatibility complex (MHC) class I molecules to promote immune evasion. We have constructed a BHV-1 UL49.5 cytoplasmic tail (CT) null and several UL49.5 luminal domain mutants in the backbone of wild-type BHV-1 or BHV-1 UL49.5 CT- null viruses and determined their relative TAP mediated peptide transport inhibition and MHC-1 down-regulation properties compared with BHV-1 wt. Based on our results, the UL49.5 luminal domain residues 30–32 and UL49.5 CT residues, together, promote efficient TAP inhibition and MHC-I down-regulation functions. In vitro, BHV-1 UL49.5 Δ30–32 CT-null virus growth property was similar to that of BHV-1 wt and like the wt UL49.5, the mutant UL49.5 was incorporated in the virion envelope and it formed a complex with gM in the infected cells

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment

Rationale: Decision-making involves two fundamental axes of control namely valence, spanning reward and punishment, and action, spanning invigoration and inhibition. We recently exploited a go/no-go task whose contingencies explicitly decouple valence and action to show that these axes are inextricably coupled during learning. This results in a disadvantage in learning to go to avoid punishment and in learning to no-go to obtain a reward. The neuromodulators dopamine and serotonin are likely to play a role in these asymmetries: Dopamine signals anticipation of future rewards and is also involved in an invigoration of motor responses leading to reward, but it also arbitrates between different forms of control. Conversely, serotonin is implicated in motor inhibition and punishment processing. Objective: To investigate the role of dopamine and serotonin in the interaction between action and valence during learning. Methods: We combined computational modeling with pharmacological manipulation in 90 healthy human volunteers, using levodopa and citalopram to affect dopamine and serotonin, respectively. Results: We found that, after administration of levodopa, action learning was less affected by outcome valence when compared with the placebo and citalopram groups. This highlights in this context a predominant effect of levodopa in controlling the balance between different forms of control. Citalopram had distinct effects, increasing participants' tendency to perform active responses independent of outcome valence, consistent with a role in decreasing motor inhibition. Conclusions: Our findings highlight the rich complexities of the roles played by dopamine and serotonin during instrumental learning. © 2013 The Author(s)

Holographic Vitrification

We establish the existence of stable and metastable stationary black hole bound states at finite temperature and chemical potentials in global and planar four-dimensional asymptotically anti-de Sitter space. We determine a number of features of their holographic duals and argue they represent structural glasses. We map out their thermodynamic landscape in the probe approximation, and show their relaxation dynamics exhibits logarithmic aging, with aging rates determined by the distribution of barriers.Comment: 100 pages, 25 figure

MEF2A regulates mGluR-dependent AMPA receptor trafficking independently of Arc/Arg3.1

© 2018 The Author(s). Differential trafficking of AMPA receptors (AMPARs) to and from the postsynaptic membrane is a key determinant of the strength of excitatory neurotransmission, and is thought to underlie learning and memory. The transcription factor MEF2 is a negative regulator of memory in vivo, in part by regulating trafficking of the AMPAR subunit GluA2, but the molecular mechanisms behind this have not been established. Here we show, via knockdown of endogenous MEF2A in primary neuronal culture, that MEF2A is specifically required for Group I metabotropic glutamate receptor (mGluR)-mediated GluA2 internalisation, but does not regulate AMPAR expression or trafficking under basal conditions. Furthermore, this process occurs independently of changes in expression of Arc/Arg3.1, a previously characterised MEF2 transcriptional target and mediator of mGluR-dependent long-term depression. These data demonstrate a novel MEF2A-dependent mechanism for the regulation of activity-dependent AMPAR trafficking