Interaction between Phrasal Structure and Vowel Tenseness in German: An Acoustic and Articulatory Study

Phrase-final lengthening affects the segments preceding a prosodic boundary. This prosodic variation is generally assumed to be independent of the phonemic identity. We refer to this as the ‘uniform lengthening hypothesis’ (ULH). However, in German, lax vowels do not undergo lengthening for word stress or shortening for increased speech rate, indicating that temporal properties might interact with phonemic identity. We test the ULH by comparing the effect of the boundary on acoustic and kinematic measures for tense and lax vowels and several coda consonants. We further examine if the boundary effect decreases with distance from the boundary. Ten native speakers of German were recorded by means of electromagnetic articulography (EMA) while reading sentences that contained six minimal pairs varying in vowel tenseness and boundary type. In line with the ULH, the results show that the acoustic durations of lax vowels are lengthened phrase-finally, similarly to tense vowels. We find that acoustic lengthening is stronger the closer the segments are to the boundary. Articulatory parameters of the closing movements toward the post-vocalic consonants are affected by both phrasal position and identity of the preceding vowel. The results are discussed with regard to the interaction between prosodic structure and vowel tenseness.

A Logic for Monitoring Dynamic Networks of Spatially-distributed Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and physical components interacting through sensors and/or actuators. Computational elements are networked at every scale and can communicate with each other and with humans. Nodes can join and leave the network at any time or they can move to different spatial locations. In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions. We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration. Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time. STREL combines the Signal Temporal Logic with two spatial modalities reach and escape that operate over the weighted graph. From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround. We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure. We provide an offline monitoring algorithm for STREL and we show the feasibility of our approach with the application to two case studies: monitoring spatio-temporal requirements over a simulated mobile ad-hoc sensor network and a simulated epidemic spreading model for COVID19.

Spatio-temporal properties of pulse propagation in a graphene quantum system

In this paper we have theoretically studied the spatial-temporal evolution of electromagnetic light propagation through a four-level graphene quantum system by using density matrix method and perturbation theory. The four-level graphene quantum medium interacted by an elliptical polarized coupling and a weak probe lights, respectively. We present the analytical solution for solving the Maxwell–Bloch equations for graphene and electromagnetic field in space and time domains. Then, we have analyzed the dynamic control of pulse propagation and optical dual switching in such a laser-driven quantum system. Our theoretical findings show that by adjusting the optical parameters such as elliptical angle i.e. phase difference between right-and-left circularly polarized, one can easily control the absorption spectrum and pulse propagation of the probe light in graphene medium. Our results may have potential applications in designing the new quantum devices for usage in quantum information processing.

COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

Using Deep Clustering to Improve fMRI Dynamic Functional Connectivity Analysis

Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly per- formed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means per- formance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occu- pancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subse- quent clustering step. We assess the use of deep autoencoders for feature selection prior to applying k-means clustering to the encoded data. We compare this deep clustering method to feature selec- tion using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clus- tering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of feature selection method has a significant effect on group-level measurements of state temporal properties. We therefore advocate for the use of deep clustering as a precursor to clustering in dFC.

Prospects for the development of law-formation in Ukraine through the prism of the application of the prognostic method of research

The author analyzes doctrinal approaches to characterizing the prospects for the development of law-formation in Ukraine. The methodological inadequacy of ensuring the study of the prospects of development of legal phenomena, including lawmaking, is noted. It is noted that law-formation as a socio-legal phenomenon is due to various factors of its development, is influenced by the peculiarities of society as a sphere of its existence, and the state as a central subject of law-making. This conditionality of the process of law formation characterizes such dialectical regularities as the presence of prospects for development and the ability to improve legally significant activities, including activities in the field of law enforcement. Prospects for the development of law-formation as its integral property, reflects the relevant qualitative changes in the process and content of the law-formation, occurring within the temporal boundaries and characterize the law-formation as a phenomenon that has the dynamics of its development. These characteristics of the law-formation are most thoroughly and comprehensively disclosed in terms of prognostic method of scientific research, because, on the one hand, based on temporal properties and due to relations between subjects, changes in their content that form the basis of law, and on the other hand, they are manifested at the level of legal institutions (the process of law-formation, norms of law, legal regulation, the subjective composition of law-making, etc.). In this regard, the issue of prospects for the development of lawmaking and ways to improve it in terms of improving the process of law formation, identification and consideration of objective laws of its development, improving the quality of law, the quality of its expression, the effectiveness of public relations is important. scientific rethinking in order to develop scientific knowledge about the prospects of law, ways to improve both the process of its formation and improve the quality of law itself. Based on the analysis of scholars' views on the problem of studying law-formation in modern conditions of development of Ukrainian society, the cognitive perspectives of application of the prognostic method of studying law-making in Ukraine are established. Prospects for the development and ways to improve law-formation as independent theoretical and legal aspects of knowledge of law education require the isolation and further application of the prognostic method of research, which is potentially able to: first, to reveal lawmaking through the prism of its development; secondly, to reveal in the most comprehensive way the objective and subjective aspects that determine the future qualitative state of the law-formation, to determine the ways of influencing the formation of law to increase its level; thirdly, to structure the development of law-formation in separate directions.

Brightness change is optimal stimulus for parasol retinal ganglion cells

Magnocellular-projecting retinal ganglion cells show spike response in two cases. Firstly, as a result of presentation of the optimal stimulus. Secondly, rebound excitation when removing the opposite stimulus. Also, there are studies suggesting that rebound excitation meets conditions to participate in visual perception at the same sensitivity and reaction speed as a response to the optimal stimulus. Thus, white noise stimulation creates possibility to catch the form of a smooth transition from one type of response to another. Using freely available data, a spike-triggered behavior map was built that does not show the area of silence between those two types of spike triggers. Moreover, linear filter with biphasic temporal properties which work as the derivative kernel demonstrate that both responses are two sides of the same coin. Thus, it is suggested to determine the optimal stimulus for magnocellular-projecting retinal ganglion cells as brightness change according to concentric center–surround receptive field structure.

Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

Deriving Spatio-Temporal Geographies of Human Mobility from GPS traces

This paper leverages on the opportunities presented by individual level GPS data to study human mobility. It develops a methodology to understand the spatio-temporal properties of collective movements using network science. Through a spatially-weighted community detection approach, we derived functional neighbourhoods from human mobility patterns from GPS data and analyse the extent to which they vary across time. The results show that while the overall city structure remains stable, functional neighbourhoods tend to contract and expand over the course of the day. This work proposes a methodological framework and emphasises the importance of detecting short-term structural changes in cities based on human mobility.

Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson’s disease

Alterations in time-varying functional connectivity (FC) have been found in Parkinson’s disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.