Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby ...future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
Many important aspects of biological knowledge at the molecular level can be represented by
. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from ...experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through
linking the
and the compounds (
, proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in
. Here we focus on R
, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool S
ARG
-X (
R
) to carry out an automated analysis of the connectivity properties of R
biochemical reaction network and of its biological hierarchy (
, cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at
; (2) show the effectiveness of our tool by providing an analysis of the R
network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of S
ARG
-X, we provide a detailed automated analysis of the R
network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within R
through easy-to-use APIs (
).
A Sense and Respond (SaR) system endows a Business Intelligence system with the intelligence needed to react timely to exogenous as well as endogenous events. To this end, a SaR system needs to know ...the Key Performance Indicators (KPIs) that must be maximized as well as their relative weights. While the first information can be easily obtained through interviews, the second one is quite hard to get. This motivates the investigation of methods and tools to address this problem.
In such a context, the main contributions of this paper are the following. First, we show how KPIs can be effectively defined using linear constraints. Second, we show how the problem of computing the actions that the SaR system proposes to the manager can be formulated as a Mixed Integer Linear Programming (MILP) problem. Third, we show how KPI weights can be computed from previous managing decisions by solving a suitable MILP problem. Fourth, we provide experimental results showing the effectiveness of the proposed approach.
The ever-increasing deployment of autonomous Cyber-Physical Systems (CPSs) (e.g., autonomous cars, UAV) exacerbates the need for efficient formal verification methods. In this setting, the main ...obstacle to overcome is the huge number of scenarios to be evaluated. Statistical Model Checking (SMC) is a simulation-based approach that holds the promise to overcome such an obstacle by using statistical methods in order to sample the set of scenarios. Many SMC tools exist, and they have been reviewed in several works. In this paper, we will overview Monte Carlo-based SMC tools in order to provide selection criteria based on Key Performance Indicators (KPIs) for the verification activity (e.g., minimize verification time or cost) as well as on the environment features, the kind of system model, the language used to define the requirements to be verified, the statistical inference approach used, and the algorithm implementing it. Furthermore, we will identify open research challenges in the field of (SMC) tools.
Cyber-physical systems are typically composed of a physical system (plant) controlled by a software (controller). Such a controller, given a plant state s and a plant action u, returns 1 iff taking ...action u in state s leads to the physical system goal or at least one step closer to it. Since a controller K is typically stored in compressed form, it is difficult for a human designer to actually understand how “good” K is. Namely, natural questions such as “does K cover a wide enough portion of the system state space?”, “does K cover the most important portion of the system state space?” or “which actions are enabled by K in a given portion of the system space?” are hard to answer by directly looking at K. This paper provides a methodology to automatically generate a picture of K as a 2D diagram, starting from a canonical representation for K and relying on available open source graphing tools (e.g., Gnuplot). Such picture allows a software designer to answer to the questions listed above, thus achieving a better qualitative understanding of the controller at hand.
Stress is a risk factor for impaired general, mental, and reproductive health. The role of physiological and supraphysiological estradiol concentrations in stress perception and stress processing is ...less well understood. We, therefore, conducted a prospective observational study to investigate the association between estradiol, stress perception, and stress-related cognitive performance within serial measurements either during the natural menstrual cycle or during fertility treatment, where estradiol levels are strongly above the physiological level of a natural cycle, and consequently, represent a good model to study dose-dependent effects of estradiol. Data from 44 women receiving
fertilization (IVF) at the Department of Reproductive Endocrinology in Zurich, Switzerland was compared to data from 88 women with measurements during their natural menstrual cycle. The German version of the Perceived Stress Questionnaire (PSQ) and the Cognitive Bias Test (CBT), in which cognitive performance is tested under time stress were used to evaluate subjective and functional aspects of stress. Estradiol levels were investigated at four different time points during the menstrual cycle and at two different time points during a fertility treatment. Cycle phases were associated with PSQ worry and cognitive bias in normally cycling women, but different phases of fertility treatment were not associated with subjectively perceived stress and stress-related cognitive bias. PSQ lack of joy and PSQ demands related to CBT in women receiving fertility treatment but not in women with a normal menstrual cycle. Only strong changes of the estradiol level during fertility treatment were weakly associated with CBT, but not with subjectively experienced stress. Our research emphasizes the multidimensional character of stress and the necessity to adjust stress research to the complex nature of stress perception and processing. Infertility is associated with an increased psychological burden in patients. However, not all phases of the process to overcome infertility do significantly increase patient stress levels. Also, research on the psychological burden of infertility should consider that stress may vary during the different phases of fertility treatment.
ClinicalTrials.gov # NCT02098668.
Interpretation of observational studies on associations between prefrontal cognitive functioning and hormone levels across the female menstrual cycle is complicated due to small sample sizes and poor ...replicability.
This observational multisite study comprised data of
= 88 menstruating women from Hannover, Germany, and Zurich, Switzerland, assessed during a first cycle and
= 68 re-assessed during a second cycle to rule out practice effects and false-positive chance findings. We assessed visuospatial working memory, attention, cognitive bias and hormone levels at four consecutive time-points across both cycles. In addition to inter-individual differences we examined intra-individual change over time (i.e., within-subject effects).
Estrogen, progesterone and testosterone did not relate to inter-individual differences in cognitive functioning. There was a significant negative association between intra-individual change in progesterone and change in working memory from pre-ovulatory to mid-luteal phase during the first cycle, but that association did not replicate in the second cycle. Intra-individual change in testosterone related negatively to change in cognitive bias from menstrual to pre-ovulatory as well as from pre-ovulatory to mid-luteal phase in the first cycle, but these associations did not replicate in the second cycle.
There is no consistent association between women's hormone levels, in particular estrogen and progesterone, and attention, working memory and cognitive bias. That is, anecdotal findings observed during the first cycle did not replicate in the second cycle, suggesting that these are false-positives attributable to random variation and systematic biases such as practice effects. Due to methodological limitations, positive findings in the published literature must be interpreted with reservation.
New approaches to ovarian stimulation protocols, such as luteal start, random start or double stimulation, allow for flexibility in ovarian stimulation at different phases of the menstrual cycle. It ...has been proposed that the success of these methods is based on the continuous growth of multiple cohorts ("waves") of follicles throughout the menstrual cycle which leads to the availability of ovarian follicles for ovarian controlled stimulation at several time points. Though several preliminary studies have been published, their scientific evidence has not been considered as being strong enough to integrate these results into routine clinical practice. This work aims at adding further scientific evidence about the efficiency of variable-start protocols and underpinning the theory of follicular waves by using mathematical modeling and numerical simulations. For this purpose, we have modified and coupled two previously published models, one describing the time course of hormones and one describing competitive follicular growth in a normal menstrual cycle. The coupled model is used to test ovarian stimulation protocols
. Simulation results show the occurrence of follicles in a wave-like manner during a normal menstrual cycle and qualitatively predict the outcome of ovarian stimulation initiated at different time points of the menstrual cycle.
Cyber-Physical Systems (CPSs), i.e. , systems comprising both software and physical components, arise in many industry-relevant application domains and often mission- or safety-critical.
System-Level ...Verification (SLV) of CPSs aims at certifying that given ( e.g. , safety or liveness) specifications are met, or at estimating the value of some Key Performance Indicators, when the system runs in its operational environment, that is in presence of inputs (from the user or other systems) and/or of additional, uncontrolled disturbances.
In order to enable SLV of complex systems from the early design phases, the currently most adopted approach envisions the simulation of a system model under the (time bounded) operational scenarios deemed of interest.
Unfortunately, simulation-based SLV can be computationally prohibitive (years of sequential simulation), since system model simulation is computationally intensive and the set of scenarios of interest can be extremely large.
In this article, we present a technique that, given a collection of scenarios of interest (extracted from mass-storage databases or from symbolic structures like constraint-based scenario generators), computes parallel shortest simulation campaigns , which drive a possibly large number of system model simulators running in parallel in a HPC infrastructure through all (and only) those scenarios in the user-defined (possibly random) order, by wisely avoiding multiple simulations of repeated trajectories, and thus minimising the overall completion time, compatibly with the available simulator memory capacity.
Our experiments on SLV of Modelica/FMU and Simulink case study models with up to almost 200 million scenarios show that our optimisation yields speedups as high as 8×. This, together with the enabled massive parallelisation , makes practically viable (a few weeks in a HPC infrastructure) verification tasks (both statistical and exhaustive, with respect to the given set of scenarios) which would otherwise take inconceivably long time.
Within electrical distribution networks, substation constraint management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation ...constraint management can be measured as the reduction of constraint violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraint management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. In this article, we present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraint management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraint management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy.
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