Anomalies of Imagination, Self-Disorders, and Schizophrenia Spectrum Psychopathology: A Network Analysis

BackgroundAnomalies of imagination encompass disturbances of the basic experiential structure of fantasies and imagery that can be explored in a semi-structured way with the Examination of Anomalous Fantasy and Imagination (EAFI). We aimed (1) to examine the distribution of anomalies of imagination among different diagnostic groups and a group of healthy controls, and (2) to examine their relation with disorders of basic self, perceptual disturbances and canonical state psychopathology of the schizophrenia-spectrum (positive, negative and general symptoms).MethodsThe 81 participants included patients with schizophrenia or other non-affective psychosis (N = 32), schizotypal personality disorder (N = 15) or other mental illness (N = 16) and healthy controls (N = 18). The assessment encompassed EAFI, Examination of Anomalous Self-Experience (EASE), parts of Bonn Scale for the Assessment of Basic Symptoms (BSABS) and Positive and Negative Syndrome Scale (PANSS). For network analysis, the associations of EAFI with the other psychopathological variables were tested by Pearson's correlation coefficient and graphically represented using multidimensional clustering. Comparisons between correlations in the network were tested with Steiger's test.ResultsAnomalies of imagination aggregated significantly in schizophrenia-spectrum disorders compared to other mental illness and healthy controls with no difference between schizophrenia and schizotypal disorder. In the network analysis, anomalies of imagination were closely inter-connected with self-disorders. Although, the anomalies of imagination correlated moderately with perceptual disturbance and positive, negative and general state symptomatology, these dimensions aggregated separately and relatively distant in the network.ConclusionsThe results support that anomalies of imagination are highly characteristic of schizophrenia-spectrum disorders and closely related to self-disorders.

Synthesis of a time-optimal control system for an extremal object

The paper presents the derivation of the synthesis method for the algorithm of the time-optimal controller for a third order dynamic system. A model with an extreme second-order transient response with delay was adopted as an object of research. The constant speed actuator is represented by an integrator. The synthesis is based on using the Pontryagin’s maximum principle and describing the dynamics of a system in the state space using canonical variables. The verification of the correctness of the result obtained by the theorem of Feldbaum A.A. on the number of switchings of the direction of movement of the regulating body during the control interval has been executed. To calculate the canonical state variables, it is proposed to use the position of the regulator, the controlled value and the derivative calculated from its values, measured on real objects.

GUY NEXT DOOR AND IMPLAUSIBLY ATTRACTIVE YOUNG WOMEN: THE VISUAL FRAMES OF SOCIAL MEDIA PROPAGANDA

This study introduces a mixed-methods approach to classifying the visual frames of state-sponsored social media propaganda. We relied on Twitter’s Election Integrity data to sample five key propaganda targets of the Internet Research Agency (IRA), including Russian and American partisan groups. We manually coded profile images and subsequently applied qualitative and quantitative processing to the images. The visual motifs identified in IRA Twitter profiles allowed us to explore how their operations deviated from canonical state propaganda marked by symbols of national identify and heroic masculinity. Indeed, the results show that the visual frames employed by the Internet Research Agency are designed to embody the vox populi with relatable, familiar, or attractive faces of ordinary people. The results also show that IRA influence operations displayed cultural acuity and familiarity with the social identity of their targets, and that the visual narrative it crafted trafficked primarily in the tropes of regular guys or implausibly attractive young women. We discuss these findings and argue that state propaganda has effectively attuned to both subcultural and visual affordances of social platforms.

Guy next door and implausibly attractive young women: The visual frames of social media propaganda

This study combines data analysis with multilevel processing of visual communication to classify the visual frames of state-sponsored social media propaganda. We relied on Twitter’s Election Integrity data to sample five propaganda targets of the Internet Research Agency, including Russian and American partisan groups, and explored how their operations deviated from canonical state propaganda marked by symbols of national identity and heroic masculinity. The results show that the visual frames employed by the Internet Research Agency are designed to embody the vox populi with relatable, familiar, or attractive faces of ordinary people. The results also indicate that Internet Research Agency influence operations displayed cultural acuity and familiarity with the social identity of their targets, and that the visual narrative the agency crafted trafficked primarily in the tropes of regular guys or attractive young women. We discuss these findings and argue that state-sponsored propaganda has attuned to subcultural and visual affordances of social platforms.

Dimension reduction in recurrent networks by canonicalization

<p style='text-indent:20px;'>Many recurrent neural network machine learning paradigms can be formulated using state-space representations. The classical notion of canonical state-space realization is adapted in this paper to accommodate semi-infinite inputs so that it can be used as a dimension reduction tool in the recurrent networks setup. The so-called input forgetting property is identified as the key hypothesis that guarantees the existence and uniqueness (up to system isomorphisms) of canonical realizations for causal and time-invariant input/output systems with semi-infinite inputs. Additionally, the notion of optimal reduction coming from the theory of symmetric Hamiltonian systems is implemented in our setup to construct canonical realizations out of input forgetting but not necessarily canonical ones. These two procedures are studied in detail in the framework of linear fading memory input/output systems. {Finally, the notion of implicit reduction using reproducing kernel Hilbert spaces (RKHS) is introduced which allows, for systems with linear readouts, to achieve dimension reduction without the need to actually compute the reduced spaces introduced in the first part of the paper.</p>

Direct visualization of translational GTPase factor pool formed around the archaeal ribosomal P-stalk by high-speed AFM

In translation elongation, two translational guanosine triphosphatase (trGTPase) factors EF1A and EF2 alternately bind to the ribosome and promote polypeptide elongation. The ribosomal stalk is a multimeric ribosomal protein complex which plays an essential role in the recruitment of EF1A and EF2 to the ribosome and their GTP hydrolysis for efficient and accurate translation elongation. However, due to the flexible nature of the ribosomal stalk, its structural dynamics and mechanism of action remain unclear. Here, we applied high-speed atomic force microscopy (HS-AFM) to directly visualize the action of the archaeal ribosomal heptameric stalk complex, aP0•(aP1•aP1)3(P-stalk). HS-AFM movies clearly demonstrated the wobbling motion of the P-stalk on the large ribosomal subunit where the stalk base adopted two conformational states, a predicted canonical state, and a newly identified flipped state. Moreover, we showed that up to seven molecules of archaeal EF1A (aEF1A) and archaeal EF2 (aEF2) assembled around the ribosomal P-stalk, corresponding to the copy number of the common C-terminal factor-binding site of the P-stalk. These results provide visual evidence for the factor-pooling mechanism by the P-stalk within the ribosome and reveal that the ribosomal P-stalk promotes translation elongation by increasing the local concentration of translational GTPase factors.

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

Direct visualization of translational GTPase factor-pool formed around the archaeal ribosomal P-stalk by high-speed atomic force microscopy

The ribosomal stalk protein plays an essential role in the recruitment of translational GTPase factors EF1A and EF2 to the ribosome and their GTP hydrolysis for efficient translation elongation. However, due to the flexible nature of the ribosomal stalk, its structural dynamics and mechanism of action remain unclear. Here, we applied high-speed atomic force microscopy (HS-AFM) to directly visualize the action of the archaeal ribosomal stalk (P-stalk). HS-AFM movies clearly demonstrated the wobbling motion of the P-stalk on the large ribosomal subunit, where the stalk base adopted two conformational states, a predicted canonical state, and a newly identified flipped state. Intriguingly, archaeal aEF1A and aEF2 molecules spontaneously assembled around the ribosomal P-stalk up to the maximum number of available binding sites. These results provide the first visual evidence for the factor-pooling mechanism and reveal that the ribosomal P-stalk promotes translation elongation by increasing the local concentration of translational GTPase factors.

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.