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1. The role of V3 neurons in speed-dependent interlimb coordination during locomotion in mice

   https://doi.org/10.7554/eLife.73424  

   Zhang, Han; Shevtsova, Natalia A; Deska-Gauthier, Dylan; Mackay, Colin; Dougherty, Kimberly J; Danner, Simon M; Zhang, Ying; Rybak, Ilya A (April 2022, eLife)

   Speed-dependent interlimb coordination allows animals to maintain stable locomotion under different circumstances. The V3 neurons are known to be involved in interlimb coordination. We previously modeled the locomotor spinal circuitry controlling interlimb coordination (Danner et al., 2017). This model included the local V3 neurons that mediate mutual excitation between left and right rhythm generators (RGs). Here, our focus was on V3 neurons involved in ascending long propriospinal interactions (aLPNs). Using retrograde tracing, we revealed a subpopulation of lumbar V3 aLPNs with contralateral cervical projections. V3 OFF mice, in which all V3 neurons were silenced, had a significantly reduced maximal locomotor speed, were unable to move using stable trot, gallop, or bound, and predominantly used a lateral-sequence walk. To reproduce this data and understand the functional roles of V3 aLPNs, we extended our previous model by incorporating diagonal V3 aLPNs mediating inputs from each lumbar RG to the contralateral cervical RG. The extended model reproduces our experimental results and suggests that locally projecting V3 neurons, mediating left–right interactions within lumbar and cervical cords, promote left–right synchronization necessary for gallop and bound, whereas the V3 aLPNs promote synchronization between diagonal fore and hind RGs necessary for trot. The model proposes the organization of spinal circuits available for future experimental testing. 

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2. Experience Migrating a Pipeline for the C-MĀIKI gateway from Tapis v2 to Tapis v3

   https://doi.org/10.1145/3491418.3535139  

   Wong, Yick Ching; Cleveland, Sean B.; Jacobs, Gwen A. (July 2022, Practice and Experience in Advanced Research Computing)

   The C-MĀIKI gateway is a science gateway that leverages a computational workload management API called Tapis to support modern, interoperable, and scalable microbiome data analysis. This project is focused on migrating an existing C-MĀIKI gateway pipeline from Tapis v2 to Tapis v3 so that it can take advantage of the new robust Tapis v3 features and stay modern. This requires three major steps: 1) Containerization of each existing microbiome workflow. 2) Create a new app definition for each of the workflows. 3) Enabling the ability to submit jobs to a SLURM scheduler inside of a singularity container to support the Nextflow workflow manager. This work presents the experience and challenges in upgrading the pipeline. 

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3. Probing adsorption of methane onto vanadium cluster cations via vibrational spectroscopy

   https://doi.org/10.1063/5.0169118  

   Kozubal, Justine; Heck, Tristan; Metz, Ricardo B (November 2023, The Journal of Chemical Physics)

   Photofragment spectroscopy is used to measure the vibrational spectra of V2(+)(CH4)n (n = 1–4), V3(+)(CH4)n (n = 1–3), and Vx(+)(CH4) (x = 4–8) in the C–H stretching region (2550–3100 cm−1). Spectra are measured by monitoring loss of CH4. The experimental spectra are compared to simulations at the B3LYP+D3/6-311++G(3df,3pd) level of theory to identify the geometry of the ions. Multi-reference configuration interaction with Davidson correction (MRCI+Q) calculations are also carried out on V2(+) and V3(+). The methane binding orientation in V2(+)(CH4)n (n = 1–4) evolves from η3 to η2 as more methane molecules are added. The IR spectra of metal-methane clusters can give information on the structure of metal clusters that may otherwise be hard to obtain from isolated clusters. For example, the V3(+)(CH4)n (n = 1–3) experimental spectra show an additional peak as the second and third methane molecules are added to V3(+), which indicates that the metal atoms are not equivalent. The Vx(+)(CH4) show a larger red shift in the symmetric C–H stretch for larger clusters with x = 5–8 than for the small clusters with x = 2, 3, indicating increased covalency in the interaction of larger vanadium clusters with methane. 

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4. Evaluating generative networks using Gaussian mixtures of image features

   https://doi.org/10.1109/WACV56688.2023.00036  

   Luzi, Lorenzo; Marrero, Carlos Ortiz; Wynar, Nile; Baraniuk, Richard G.; Henry, Michael J. (February 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inception Distance (FID). FID assumes that images featurized using the penultimate layer of Inception-v3 follow a Gaussian distribution, an assumption which cannot be violated if we wish to use FID as a metric. However, we show that Inception-v3 features of the ImageNet dataset are not Gaussian; in particular, every single marginal is not Gaussian. To remedy this problem, we model the featurized images using Gaussian mixture models (GMMs) and compute the 2-Wasserstein distance restricted to GMMs. We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs. We experimentally show the advantages of WaM over FID, including how FID is more sensitive than WaM to imperceptible image perturbations. By modelling the non-Gaussian features obtained from Inception-v3 as GMMs and using a GMM metric, we can more accurately evaluate generative network performance. 

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5. Video Surveillance Framework Based on Real-Time Face Mask Detection and Recognition

   https://doi.org/10.1109/INISTA52262.2021.9548475  

   Nasiri, Ehsan; Milanova, Mariofanna; Nasiri, Ardalan (August 2021, International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2021)

   In this paper we proposed a real-time face mask detection and recognition for CCTV surveillance camera videos. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, face mask detection, and face recognition. In the first step, a set of keyframes are selected using histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (CLAHE), and poses normalization (Angular Affine Transformation). In third step, facial parts are segmented using clustering approach i.e. Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, Pixel-based Feature Extraction is performed using Yolo Nano approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. The final step is face recognition which is implemented by a Kernel-based Extreme Learning Machine with Slime Mould Optimization (SMO). Experiments conducted using Python IDLE 3.8 for the proposed Yolo Nano model and also previous works as GMM with Deep learning (GMM+DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics. 

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