Enhancing our understanding of how the brain constructs conscious emotional experiences within dynamic real life contexts necessitates ecologically valid neural models. Here, we present evidence delineating the constraints of current fMRI activation models in capturing naturalistic fear dynamics. To address this challenge, we use naturalistic fMRI with predictive modeling techniques to develop an ecologically valid fear signature that integrates activation and connectivity profiles, allowing for accurate prediction of subjective fear experience under highly dynamic close-to-real-life conditions. This signature arises from insights into the crucial role of distributed brain networks and their interactions in emotion modulation, and the potential of network-level information to improve predictions in dynamic contexts. Across a series of investigations, we demonstrate that this signature predicts stable and dynamic fear experiences across naturalistic scenarios with heightened sensitivity and specificity, surpassing traditional activation- and connectivity-based signatures. Notably, the integration of affective connectivity profiles enables accurate real-time predictions of fear fluctuations in naturalistic settings. Additionally, we unearth a distributed yet redundant brain-wide representation of fear experiences. Subjective fear is encoded not only by distributed cortical and subcortical regions but also by their interactions, with no single brain system conveying substantial unique information. Our study establishes a comprehensive and ecologically valid functional brain architecture for subjective fear in dynamic environments and bridges the gap between experimental neuroscience and real-life emotional experience.

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under quality-of-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication.

Hardware acceleration of network functions is essential to meet the challenging Quality of Service requirements in nowadays computer networks. Graphical Processing Units (GPU) are a widely deployed technology that can also be used for computing tasks, including acceleration of network functions. In this work, we demonstrate how commodity GPUs, which do not provide any network interfaces, can be used to accelerate network functions. Our approach leverages PCIe peer-to-peer capabilities and allows the GPU to control the network interface card directly, without any assistance from the operating system or control application. The presented evaluation results demonstrate the feasibility of our approach and its performance of up to 10 Gbit/s, even for small packets.

Connected plane graphs enable many local algorithmic solutions for data communication, task coordination and network maintenance in wireless sensor networks, sensor-actuator networks and distributed robotics. We study construction of such graphs by removing edges from a given network graph. We assume redundancy and coexistence, a graph structure which holds in realistic wireless network models with high probability and which assures that a connected plane graph can always be constructed with local edge removal rules. We present a local algorithm to construct such a connected plane graph. We prove algorithm correctness under redundancy and coexistence assumption. Furthermore, we discuss how far redundancy and coexistence could be weakened while still assuring correctness of the algorithm.

MOTIVATION: An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. In a simulation study, we compare the power and computation times of four different approaches for global testing. The applicability of one of these methods to gene expression data is demonstrated for the first time. In addition, we propose an algorithm for the detection of those genes which might be responsible for a group effect. RESULTS: We could detect that the power of three of the approaches is comparable in many settings but considerable differences were detected in the computation times. Our proposed gene selection algorithm was able to detect potentially effect-causing genes in artificial sets with high power when many genes were altered with a small effect, while classical multiple testing was more powerful when few genes were altered with a large effect. AVAILABILITY: An R-package called 'RepeatedHighDim' which implements our new global test procedures is made available from http://cran.r-project.org/.

The accuracy of a real time tracking system for industrial AR (IAR) applications often needs to comply with production tolerances. Such a system typically incorporates different off-/online devices so that the overall precision and accuracy cannot be trivially stated. Additionally, tracking needs to be flexible to not interfere with existing working processes and it needs to be operated and maintained free of error by on-site personnel who typically have a quality management (QM) background. For the final validation of such a complex tracking setup, empiric testing alone is either too expensive or lacks generality. This paper demonstrates a new approach to define and verify, deploy and validate, as well as to operate and maintain an IAR tracking infrastructure. We develop our concepts on the basis of an IAR application in the field of QM in the aircraft production process. It integrates a qualitative visual comparison with accurate quantitative measurements of 3D coordinates using a metrological probe. The focus is on the verification, validation, and error free operation. Monte Carlo simulation predicts the error for arbitrary system states. Using a limited set of empiric measurements in the target environment allows us to validate the simulation and thereby validate the application. This combination assures compliance of the IAR application with the required production tolerances. We show that our simulation model yields realistic results, using an in-depth analysis of an optical IR tracking system and a high-precision coordinate measurement machine capable of densely sampling the entire tracking volume. Additionally, it allows for a straightforward derivation of run-time consistency checks for the automatic identification of possible system failures. Also, estimation of the system performance during the planning and definition phases becomes possible, using the elementary accuracy specifications of the involved sensor systems.