Improving force field accuracy by training against condensed phase mixture properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulation as a route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we re-train a select number of the Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems, and assess the performance of each of these combinations. We show that retraining against the mixture data almost universally improves the force field's ability to reproduce both pure and mixture properties, reducing some systematic errors that exist when training vdW interactions against properties of pure systems only.

HyBoDT: Hybrid Bounded Distance Transforms of Trimmed NURBS Models

Distance field representation of objects in 3D space has several applications such as shape manipulation, graphics rendering, path planning, etc. Distance transforms (DTs) are discrete representations of distance fields in a regular voxel grid. The two main limitations of using distance transforms are that they are compute-intensive, and there are errors introduced while representing the object using DTs. In this work, we develop an hybrid GPU-accelerated marching wavefront method for computing DTs of models composed of trimmed NURBS surfaces with theoretical bounds. Our hybrid marching approach eliminates the error due to calculating approximate distances by marching. We also calculate the bounds on the error introduced due to the tessellation of the trimmed NURBS surfaces and calculate the propagation of these bounds in computing the DT. Finally, we present computation times for both 2D and 3D GPU DTs of test objects. We show that our GPU-accelerated approach is significantly faster than existing CPU-based methods.

Improving force field accuracy by training against condensed phase mixture properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulation as a route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we re-train a select number of the Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems, and assess the performance of each of these combinations. We show that retraining against the mixture data almost universally improves the force field's ability to reproduce both pure and mixture properties, reducing some systematic errors that exist when training vdW interactions against properties of pure systems only.

Electrocardio Panorama: Synthesizing New ECG views with Self-supervision

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.

Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion

We propose an audio-driven talking-head method to generate photo-realistic talking-head videos from a single reference image. In this work, we tackle two key challenges: (i) producing natural head motions that match speech prosody, and (ii)} maintaining the appearance of a speaker in a large head motion while stabilizing the non-face regions. We first design a head pose predictor by modeling rigid 6D head movements with a motion-aware recurrent neural network (RNN). In this way, the predicted head poses act as the low-frequency holistic movements of a talking head, thus allowing our latter network to focus on detailed facial movement generation. To depict the entire image motions arising from audio, we exploit a keypoint based dense motion field representation. Then, we develop a motion field generator to produce the dense motion fields from input audio, head poses, and a reference image. As this keypoint based representation models the motions of facial regions, head, and backgrounds integrally, our method can better constrain the spatial and temporal consistency of the generated videos. Finally, an image generation network is employed to render photo-realistic talking-head videos from the estimated keypoint based motion fields and the input reference image. Extensive experiments demonstrate that our method produces videos with plausible head motions, synchronized facial expressions, and stable backgrounds and outperforms the state-of-the-art.

Coherence Resonance in Random Erdös-Rényi Neural Networks: Mean-Field Theory

Additive noise is known to tune the stability of nonlinear systems. Using a network of two randomly connected interacting excitatory and inhibitory neural populations driven by additive noise, we derive a closed mean-field representation that captures the global network dynamics. Building on the spectral properties of Erdös-Rényi networks, mean-field dynamics are obtained via a projection of the network dynamics onto the random network’s principal eigenmode. We consider Gaussian zero-mean and Poisson-like noise stimuli to excitatory neurons and show that these noise types induce coherence resonance. Specifically, the stochastic stimulation induces coherent stochastic oscillations in the γ-frequency range at intermediate noise intensity. We further show that this is valid for both global stimulation and partial stimulation, i.e. whenever a subset of excitatory neurons is stimulated only. The mean-field dynamics exposes the coherence resonance dynamics in the γ-range by a transition from a stable non-oscillatory equilibrium to an oscillatory equilibrium via a saddle-node bifurcation. We evaluate the transition between non-coherent and coherent state by various power spectra, Spike Field Coherence and information-theoretic measures.

Field Representation Microwave Thermography Utilizing Lossy Microwave Design Materials

In this contribution, we are investigating a technique for the representation of electromagnetic fields by recording their thermal footprints on an indicator material using a thermal camera. Fundamentals regarding the interaction of electromagnetic heating, thermodynamics, and fluid dynamics are derived which allow for a precise design of the field illustration method. The synthesis and description of high-loss dielectric materials is discussed and a technique for a simple estimation of the broadband material’s imaginary permittivity part is introduced. Finally, exemplifying investigations, comparing simulations and measurements on the fundamental TE10-mode in an X-band waveguide are presented, which prove the above introduced sensing theory.

Simultaneous changes in visual acuity, cortical population receptive field size, visual field map size, and retinal thickness in healthy human aging

Healthy human aging is associated with a deterioration of visual acuity, retinal thinning, visual field map shrinkage and increasing population receptive field sizes. Here we ask how these changes are related to each other in a cross-sectional sample of fifty healthy adults aged 20–80 years. We hypothesized that age-related loss of macular retinal ganglion cells may lead to decreased visual field map sizes, and both may lead to increased pRF sizes in the cortical central visual field representation. We measured our participants’ perceptual corrected visual acuity using standard ophthalmological letter charts. We then measured their early visual field map (V1, V2 and V3) functional population receptive field (pRF) sizes and structural surface areas using fMRI, and their retinal structure using high-definition optical coherence tomography. With increasing age visual acuity decreased, pRF sizes increased, visual field maps surface areas (but not whole-brain surface areas) decreased, and retinal thickness decreased. Among these measures, only functional pRF sizes predicted perceptual visual acuity, and Bayesian statistics support a null relationship between visual acuity and cortical or retinal structure. However, pRF sizes were in turn predicted by cortical structure only (visual field map surface areas), which were only predicted by retinal structure (thickness). These results suggest that simultaneous disruptions of neural structure and function throughout the early visual system may underlie the deterioration of perceptual visual acuity in healthy aging.

The Mechanism of Macular Sparing

Patients with homonymous hemianopia sometimes show preservation of the central visual fields, ranging up to 10°. This phenomenon, known as macular sparing, has sparked perpetual controversy. Two main theories have been offered to explain it. The first theory proposes a dual representation of the macula in each hemisphere. After loss of one occipital lobe, the back-up representation in the remaining occipital lobe is postulated to sustain ipsilateral central vision in the blind hemifield. This theory is supported by studies showing that some midline retinal ganglion cells project to the wrong hemisphere, presumably driving neurons in striate cortex that have ipsilateral receptive fields. However, more recent electrophysiological recordings and neuroimaging studies have cast doubt on this theory by showing only a minuscule ipsilateral field representation in early visual cortical areas. The second theory holds that macular sparing arises because the occipital pole, where the macula is represented, remains perfused after occlusion of the posterior cerebral artery because it receives collateral flow from the middle cerebral artery. An objection to this theory is that it cannot account for reports of macular sparing in patients after loss of an entire occipital lobe. On close scrutiny, such reports turn out to be erroneous, arising from inadequate control of fixation during visual field testing. Patients seem able to detect test stimuli on their blind side within the macula or along the vertical meridian because they make surveillance saccades. A purported treatment for hemianopia, called vision restoration therapy, is based on this error. The dual perfusion theory is supported by anatomical studies showing that the middle cerebral artery perfuses the occipital pole in many individuals. In patients with hemianopia from stroke, neuroimaging shows preservation of the occipital pole when macular sparing is present. The frontier dividing the infarcted territory of the posterior cerebral artery and the preserved territory of the middle cerebral artery is variable, but always falls within the representation of the macula, because the macula is so highly magnified. For physicians, macular sparing was an important neurological sign in acute hemianopia because it signified a posterior cerebral artery occlusion. Modern neuroimaging has supplanted the importance of that clinical sign but at the same time confirmed its validity. For patients, macular sparing remains important because it mitigates the impact of hemianopia and preserves the ability to read fluently. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.