ELECTRONIC STRUCTURE AND DYNAMICS OF URANYL-PEROXIDE SPECIES

Uranyl-peroxide nanocapsules are a unique family of self-assembled actinide species. Uranyl ions rapidly self-assemble in basic peroxidic media through a myriad of reactions to coalesce into a single nanocapsule that includes both peroxide and hydroxide bridging groups between the uranyl moieties. A wide variety of capsules can be formed, and it has been proposed that square and pentagonal building blocks assemble prior to nanocapsule formation. We have studied the speciation of the pentagonal 2) uranyl-peroxide nanocapsule building blocks using density functional theory calculations. We predicted the most favorable speciation pathways for the self-assembly of the building blocks prior to cluster formation including the effect of pH, temperature, and alkali counterions. In addition, we also mapped the potential energy surface by scanning the molecular normal modes and created a large database containing uranyl monomers We then used the atomistic machine learning package to train a neural network potential in order to create a cheap structure-energy connection that could be used to predict quantum mechanics energetics of larger uranyl-peroxide systems for a fraction of the computational cost

Body Composition and Anthropometric Changes During a 10-week Training Academy in Police Recruits

Obesity and cardiometabolic risk factors are often present in law enforcement personnel, which may compromise physical readiness and long-term health. As such, physical fitness interventions are warranted for promoting officers\u27 performance and wellbeing. PURPOSE: To determine the body composition and anthropometric changes experienced by police recruits undergoing a departmental training academy. METHODS: Twenty-one police recruits (20 M, 1 F; age: 25.1 ± 5.0 y; BMI: 27.8 ± 4.3 kg/m2) were tested before and after a 10-week training academy in Lubbock, Texas. Supervised physical training was conducted 5 times per week and consisted of ~1–1.5 hours of high-intensity, multi-modal (i.e., running, weightlifting, calisthenics), functional training following linear periodization. Dual-energy X-ray absorptiometry (DXA; GE Lunar iDXA) and 3-dimensional optical imaging (3DO; Size Stream SS20) were performed to assess body composition and anthropometry. Paired-samples t-tests were performed to compare values before and after the training academy, and Cohen’s d effect sizes were generated. After Bonferroni correction, statistical significance was accepted at p\u3c0.003. Changes are presented as mean ± SD. RESULTS: From DXA, statistically significant decreases in total fat mass (FM; -3.3 ± 3.1 kg, p\u3c0.001, d=1.1), trunk FM (-2.1 ± 2.2 kg, p\u3c0.001, d=1.0), arms FM (-0.3 ± 0.3 kg, p=0.001, d=1.1), legs FM (-0.9 ± 0.9 kg, p\u3c0.001, d=1.1), and body fat percentage (-3.1 ± 2.5%, p\u3c0.001, d=1.2) were observed. Increases in total lean soft tissue (LST; 1.3 ± 1.3 kg, p=0.002, d=1.0) and trunk LST (0.8 ± 0.9 kg, p\u3c0.001, d=0.9) were also noted, with trends for increases in leg LST (0.2 ± 0.7 kg, p=0.096, d=0.4) and arm LST (0.2 ± 0.4, p=0.04, d=0.5). Decreases in 3DO abdomen circumference (-3.5 ± 3.8 cm, p\u3c0.001, d=0.9) and hip circumference (-2.2 ± 2.2 cm, p\u3c0.001, d=1.0) were noted, with trends for decreases in the circumferences of the waist (-2.4 ± 3.6 cm, p=0.007, d=0.7) and upper arm (-0.9 ± 1.5 cm, p=0.02, d=0.6). No significant changes in thigh circumference (-0.7 ± 1.9 cm, p=0.12, d=0.4) or calf circumference (-0.2 ± 1.5 cm, p=0.52, d=0.1) were noted. A trend for a decrease in body mass (-2.0 ± 3.1 kg, p=0.007, d=0.7) was also observed. CONCLUSION: The present study indicates that police academy training significantly improves recruits\u27 body composition, both reducing FM and increasing LST, which has the potential to positively affect operational performance. Future studies should track these changes over time to help develop ongoing health and fitness strategies for career police officers, ultimately improving their long-term wellbeing and job readiness

Structure, Properties, and Reactivity of Polyoxocationic Zirconium and Hafnium Clusters

Hexameric tetravalent zirconium and hafnium molecular metal oxides clusters are key building blocks of many metal-organic frameworks; however, the chemical space to form all possible MOF nodes is vast, containing many potential new clusters. Our computational study provides a complete picture of the structure, properties, and reactivity of two tetrameric zirconium and hafnium [M4(μ2-η2:η2-O2)x(μ2-OH)8-2x(H2O)16]8+ polycationic clusters. The electronic structure of the studied species has the characteristic polyoxometalate oxygen-based and metal-based bands in the valence region. The energetics for the evolution of pure metal clusters into mixed-metal clusters revealed that only the incorporation of zirconium into hafnium clusters is thermodynamically favorable. We confirmed that the incorporation of up to four peroxide ligands is thermodynamically favorable; however, the experimental absence of rich peroxide species with three or more peroxides is attributed to their thermal degradation. The mechanism for peroxide incorporation involves the partial dissociation of the cluster rather than complete dissociation

Foveated convolutions: improving spatial transformer networks by modelling the retina

Spatial Transformer Networks (STNs) have the potential to dramatically improve performance of convolutional neural networks in a range of tasks. By ‘focusing’ on the salient parts of the input using a differentiable affine transform, a network augmented with an STN should have increased performance, efficiency and interpretability. However, in practice, STNs rarely exhibit these desiderata, instead converging to a seemingly meaningless transformation of the input. We demonstrate and characterise this localisation problem as deriving from the spatial invariance of feature detection layers acting on extracted glimpses. Drawing on the neuroanatomy of the human eye we then motivate a solution: foveated convolutions. These parallel convolutions with a range of strides and dilations introduce specific translational variance into the model. In so doing, the foveated convolution presents an inductive bias, encouraging the subject of interest to be centred in the output of the attention mechanism, giving significantly improved performance

Spatial and colour opponency in anatomically constrained deep networks

Colour vision has long fascinated scientists, who have sought to understand both the physiology of the mechanics of colour vision and the psychophysics of colour perception. We consider representations of colour in anatomically constrained convolutional deep neural networks. Following ideas from neuroscience, we classify cells in early layers into groups relating to their spectral and spatial functionality. We show the emergence of single and double opponent cells in our networks and characterise how the distribution of these cells changes under the constraint of a retinal bottleneck. Our experiments not only open up a new understanding of how deep networks process spatial and colour information, but also provide new tools to help understand the black box of deep learning

Anatomically constrained ResNets exhibit opponent receptive fields; so what?

Primate visual systems are well known to exhibit varying degrees of bottlenecks in the early visual pathway. Recent works have shown that the presence of a bottleneck between 'retinal' and 'ventral' parts of artificial models of visual systems, simulating the optic nerve, can cause the emergence of cellular properties that have been observed in primates: namely centre-surround organisation and opponency. To date, however, state-of-the-art convolutional network architectures for classification problems have not incorporated such an early bottleneck. In this paper, we ask what happens if such a bottleneck is added to a ResNet-50 model trained to classify the ImageNet data set. Our experiments show that some of the emergent properties observed in simpler models still appear in these considerably deeper and more complex models, however, there are some notable differences particularly with regard to spectral opponency. The introduction of the bottleneck is experimentally shown to introduce a small but consistent shape bias into the network. Tight bottlenecks are also shown to only have a very slight affect on the top-1 accuracy of the models when trained and tested on ImageNet

How convolutional neural network architecture biases learned opponency and colour tuning

Recent work suggests that changing Convolutional Neural Network (CNN) architecture by introducing a bottleneck in the second layer can yield changes in learned function.To understand this relationship fully requires a way of quantitatively comparing trained networks.The fields of electrophysiology and psychophysics have developed a wealth of methods for characterising visual systems which permit such comparisons.Inspired by these methods, we propose an approach to obtaining spatial and colour tuning curves for convolutional neurons, which can be used to classify cells in terms of their spatial and colour opponency.We perform these classifications for a range of CNNs with different depths and bottleneck widths.Our key finding is that networks with a bottleneck show a strong functional organisation: almost all cells in the bottleneck layer become both spatially and colour opponent, cells in the layer following the bottleneck become non-opponent.The colour tuning data can further be used to form a rich understanding of how colour is encoded by a network.As a concrete demonstration, we show that shallower networks without a bottleneck learn a complex non-linear colour system, whereas deeper networks with tight bottlenecks learn a simple channel opponent code in the bottleneck layer.We further develop a method of obtaining a hue sensitivity curve for a trained CNN which enables high level insights that complement the low level findings from the colour tuning data.We go on to train a series of networks under different conditions to ascertain the robustness of the discussed results.Ultimately, our methods and findings coalesce with prior art, strengthening our ability to interpret trained CNNs and furthering our understanding of the connection between architecture and learned representation.Trained models and code for all experiments are available at https://github.com/ecs-vlc/opponency