Please rewrite the below three paragraphs into 1-2 paragraphs:

1."Emotion recognition has been drawing the attention of researchers and practitioners in recent years. While various research studies show successful applications to recognize and distinguish emotions based on physiological responses using machine learning techniques, less research to date is focused on the network properties of physiological interactions under different emotional states. To this end, we propose a multi-modal graph learning framework to quantify the interactions among physiological systems and present representative networks associated with emotional states. More specifically, we introduce a novel information-theoretic-based time delay stability to quantify complex interactions between physiological modalities. We test our quantification approach on three publicly available benchmark databases for emotion recognition and demonstrate the comparative performances of measuring the interactions of physiological systems in response to emotional states. Finally, we present the visualization of multi-modal physiological network topology, which may be useful for emotional interpretations in practice."

2."Machine learning algorithms based on physiological responses are popular computational tools for studies in affective computing and emotion recognition tasks. Most studies concentrate on applying data-driven black-box methods without explaining how to generate such accurate results, which leads to misusing and lack of trust. Therefore, balancing between model accuracy and interpretability is urgent for emotion analysis of multi-modal physiological signals. The main challenges in machine learning applications to affective computing tasks are (1) effectively quantifying physiological interactions among organ systems in association with emotional states and (2) discovering network patterns for decoding human emotional states. To this end, it is useful to develop an effective method to extract informative patterns from physiological interactions in a human-readable mode. In this paper, we present a novel multi-modal physiological network model, motivated by a combination of randomized Gaussian mutual information time delay stability, quantifying physiological interactions reasonably, and explainable machine learning components to achieve a good trade-off of accuracy and interpretability. We illustrate the capability of the proposed method to learn momentous physiological interactions and perform superiorly on two benchmark emotional datasets. We also visualize connectivity patterns of understandable physiological categories, mainly to explore the associations between organ systems and two-dimensional emotional states."

3."Objective: In clinical practice, imaging protocol settings are adjusted to the preferences of local clinicians, patient tolerance, and imaging equipment availability. Computer vision metrics (CVMs) are tools that have been shown to assess similarity in medical images. We hypothesize that CVMs could be applied to variations in clinical imaging protocols to identify shorter scan time that do not impact clinical utility of images.  We test this hypothesis using Multi-echo Complex Total Field Inversion (mcTFI) Quantitative Susceptibility Mapping MRI (QSM).
Methods: Under an IRB approved protocol, 54 subjects who had undergone clinical MRI containing the 11 times to echo (TE) 3D Gradient Recalled Echo (mGRE) required to generate mcTFI QSM. All mGRE were collected on a 3T scanner with a 32-channel head coil. We proposed a battery of popular CVMs to assess similarity between the mcTFI QSM images generated using 11 echoes (reference) and mcTFI QSM images generated from subsets of the obtained 11 TE mGRE data. Wilcoxon signed-rank significance statistical differences were then used to identify optimal subgroups of TEs most like the reference images. 
Results: All 14 CVMs studied supported dropping at least 1 TE, while 7 supported dropping the last 9 TEs. The other seven CVMs demonstrated different subgroups of TEs to result in optimal metric for each CVM.
Conclusions: This research establishes the feasibility of employing CVMs to identify comparable imaging techniques, potentially facilitating the offering of shorter imaging scans without compromising diagnostic accuracy. The study contributes a novel quantification method for determining optimal TEs based on these metrics. The approach presented also has implications for enhancing patient experience and improving scanner utilization. This study underscored the potential of CVMs to shape the future of medical imaging protocols, optimizing scan times based on desired imaging features across a spectrum of techniques."

Assuming that the readers will not be familiar with the field. Please write in an unbiased, comprehensive, and scholarly tone. The word limit is 310.