Dataset on broadband electrochemical impedance spectroscopy of Lithium-Ion batteries for different values of the state-of-charge

This dataset consists of electrochemical impedance spectroscopy measurements on commonly-used batteries, namely Samsung ICR18650-26J cylindrical Lithium-Ion cells. The complex impedance of the batteries was measured at a set of fourteen different frequencies from 0.05 Hz to 1000 Hz, using a random-phase multi-sine excitation signal. For each excited frequency, the current amplitude was 50 mA, resulting in a measurement uncertainty of approximately 0.1 mΩ. Six measurement repetitions are provided at ten different states-of-charge of four different brand-new batteries. Repeated EIS measurement results were obtained, for each individual battery cell, from six separate discharge cycles. All measurements were performed with the battery placed in a temperature-controlled chamber at 25 ± 1 °C. Batteries were allowed to thermalize before each measurement

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot

Efficient Numerical Frameworks for Multi-objective Cyber Security Planning

We consider the problem of optimal investment in cyber-security by an enterprise. Optimality is measured with respect to the overall (1) monetary cost of implementation, (2) negative side-effects of cyber-security controls (indirect costs), and (3) mitigation of the cyber-security risk. We consider “passive” and “reactive” threats, the former representing the case where attack attempts are independent of the defender’s plan, the latter, where attackers can adapt and react to an implemented cyber-security defense. Moreover, we model in three different ways the combined effect of multiple cyber-security controls, depending on their degree of complementarity and correlation. We also consider multi-stage attacks and the potential correlations in the success of different stages. First, we formalize the problem as a non-linear multi-objective integer programming. We then convert them into Mixed Integer Linear Programs (MILP) that very efficiently solve for the exact Pareto-optimal solutions even when the number of available controls is large. In our case study, we consider 27 of the most typical security controls, each with multiple intensity levels of implementation, and 37 common vulnerabilities facing a typical SME. We compare our findings against expert-recommended critical controls. We then investigate the effect of the security models on the resulting optimal plan and contrast the merits of different security metrics. In particular, we show the superior robustness of the security measures based on the “reactive” threat model, and the significance of the hitherto overlooked role of correlations

Disruption of White Matter Integrity in Bipolar Depression as a Possible Structural Marker of Illness

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Structural neural networks subserving oculomotor function in first-episode schizophrenia

BACKGROUND: Smooth pursuit and antisaccade abnormalities are well documented in schizophrenia, but their neuropathological correlates remain unclear. METHODS: In this study, we used statistical parametric mapping to investigate the relationship between oculomotor abnormalities and brain structure in a sample of first-episode schizophrenia patients (n = 27). In addition to conventional volumetric magnetic resonance imaging, we also used magnetization transfer ratio, a technique that allows more precise tissue characterization. RESULTS: We found that smooth pursuit abnormalities were associated with reduced magnetization transfer ratio in several regions, predominantly in the right prefrontal cortex. Antisaccade errors correlated with gray matter volume in the right medial superior frontal cortex as measured by conventional magnetic resonance imaging but not with magnetization transfer ratio. CONCLUSIONS: These preliminary results demonstrate that specific structural abnormalities are associated with abnormal eye movements in schizophrenia

How possible is the development of an operational psychometric method to assess the presence of the 5-HTTLPR s allele? Equivocal preliminary findings

<p>Abstract</p> <p>Objective</p> <p>The s allele of the 5-hydroxytryptamine transporter-linked promoter region (5-HTTLPR) polymorphism of the serotonin transporter gene has been found to be associated with neuroticism-related traits, affective temperaments and response to selective serotonin reuptake inhibitor (SSRI) treatment. The aim of the current study was to develop a psychometric tool that could at least partially substitute for laboratory testing and could predict the presence of the s allele.</p> <p>Methods</p> <p>The study included 138 women of Caucasian origin, mean 32.20 ± 1.02 years old. All subjects completed the Hungarian standardised version of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire (TEMPS-A) instrument and were genotyped for 5-HTTLPR using PCR. The statistical analysis included the calculation of the Index of Discrimination (D), Discriminant Function Analysis, creation of scales on the basis of the above and then item analysis and calculation of sensitivity and specificity.</p> <p>Results</p> <p>Four indices were eventually developed, but their psychometric properties were relatively poor and their joint application did not improve the outcome.</p> <p>Conclusions</p> <p>We could not create a scale that predicts the 5-HTTLPR genotype with sufficient sensitivity and specificity, therefore we could not substitute a psychometric scale for laboratory genetic testing in predicting genotype, and also possibly affective disorder characterisation and treatment.</p