Excited-state Forces within a First-principles Green's Function Formalism

We present a new first-principles formalism for calculating forces for optically excited electronic states using the interacting Green's function approach with the GW-Bethe Salpeter Equation method. This advance allows for efficient computation of gradients of the excited-state Born-Oppenheimer energy, allowing for the study of relaxation, molecular dynamics, and photoluminescence of excited states. The approach is tested on photoexcited carbon dioxide and ammonia molecules, and the calculations accurately describe the excitation energies and photoinduced structural deformations.Comment: 2 figures and 2 table

Capture–Collapse Heterocyclization:1,3-Diazepanes by C–N Reductive Elimination from Rhodacyclopentanones

Rhodacyclopentanones derived from carbonylative C–C activation of cyclopropyl ureas can be “captured” by pendant nucleophiles prior to “collapse” to 1,3-diazepanes. The choice of N-substituent on the cyclopropane unit controls the oxidation level of the product, such that C4–C5 unsaturated or saturated systems can be accessed selectively

Mercury's Internal Structure

We describe the current state of knowledge about Mercury's interior structure. We review the available observational constraints, including mass, size, density, gravity field, spin state, composition, and tidal response. These data enable the construction of models that represent the distribution of mass inside Mercury. In particular, we infer radial profiles of the pressure, density, and gravity in the core, mantle, and crust. We also examine Mercury's rotational dynamics and the influence of an inner core on the spin state and the determination of the moment of inertia. Finally, we discuss the wide-ranging implications of Mercury's internal structure on its thermal evolution, surface geology, capture in a unique spin-orbit resonance, and magnetic field generation.Comment: 36 pages, 11 figures, in press, to appear in "Mercury - The View after MESSENGER", S. C. Solomon, B. J. Anderson, L. R. Nittler (editors), Cambridge University Pres

Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE

The role of the individual in the coming era of process-based therapy

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.Accepted manuscrip

Relationships Between Implicit Power Motivation, Implicit Sexual Motivation, and Gonadal Steriod Hormones: Behavioral, Endocrine, and fMRI Investigations in Humans.

The present research explored relationships between individual differences in motivational states (basal testosterone levels (study 1), implicit power motivation (study 2), implicit sexual motivation (study 3)) and both biology and behavior (brain responses to dominance signals (study 1), steroid hormone levels (study 2), and operant conditioning (study 3). Testosterone is positively linked to dominance behavior in men. However, little is known about the moderating effects of testosterone in the human brain in the context of dominance. Study 1 used fMRI to measure amygdala BOLD response to interpersonal dominance signals of threat (anger faces) as a function of endogenous testosterone levels in 24 participants (10 men). Men’s, but not women’s, amygdala BOLD response to anger faces was negatively correlated with their endogenous testosterone levels. Study 2 investigated basal and dynamic relationships between implicit power motivation (n Power) and both salivary estradiol and testosterone in women. During a laboratory dominance contest, participants competed in pairs on a cognitive task and contest outcome (win vs. loss) was experimentally varied. Estradiol and testosterone levels were determined in saliva samples collected at baseline and several times post-contest, including one day post-contest. n Power was positively associated with basal estradiol concentrations. Women’s estradiol responses to a dominance contest were influenced by the interaction of n Power and contest outcome: Estradiol increased in power-motivated winners but decreased in power-motivated losers. Lastly, n Power and estradiol did not correlate with self-reported dominance and correlated negatively with self-reported aggression. Self-reported dominance and aggression did not predict estradiol changes as a function of contest outcome. Overall, n Power did not predict basal testosterone levels or testosterone changes as a function of dominance contest outcome. In study 3, using a newly-created method, implicit sexual motivation themes were coded in participants’ creative stories. To assess the predictive validity of coding implicit sexual motivation in creative stories, an operant conditioning paradigm was employed, which assessed the rewarding properties of visual sexual stimuli. Implicit sexual motivation was positively associated with implicit learning to achieve visual sexual reward, and this effect was particularly strong in men.Ph.D.PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61654/1/stantons_1.pd

Manganese Oxide Thin Films Prepared by Nonaqueous Sol-Gel Processing: Preferential Formation of Birnessite

High quality manganese oxide thin films with smooth surfaces and even thicknesses have been prepared with a nonaqueous sol–gel process involving reduction of tetraethylammonium permanganate in methanol. Spin-coated films have been cast onto soft glass, quartz, and Ni foil substrates, with two coats being applied for optimum crystallization. The addition of alkali metal cations as dopants results in exclusive formation of the layered birnessite phase. By contrast, analogous reactions in bulk sol–gel reactions yield birnessite, tunneled, and spinel phases depending on the dopant cation. XRD patterns confirm the formation of well-crystallized birnessite. SEM images of Li-, Na-, and K–birnessite reveal extremely smooth films having uniform thickness of less than 0.5 μm. Thin films of Rb– and Cs–birnessite have more fractured and uneven surfaces as a result of some precipitation during the sol–gel transformation. All films consist of densely packed particles of about 0.1 μm. When tetrabutylammonium permanganate is used instead of tetraethylammonium permanganate, the sol–gel reaction yields amorphous manganese oxide as the result of diluted Mn sites in the xerogel film. Bilayer films have been prepared by casting an overcoat of K–birnessite onto an Na–birnessite film. However, Auger depth profiling indicates considerable mixing between the adjacent layers

Kernel Matrix-Based Heuristic Multiple Kernel Learning

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods