Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.Comment: For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of databas

CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. Accelerating Monte Carlo algorithms that rely on random sampling with such CMOS+X technologies could have significant impact on a large number of fields from probabilistic machine learning, optimization to quantum simulation. In this paper, we show the combination of stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile Field Programmable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Our approach enables high-quality true randomness that is essential for Monte Carlo based probabilistic sampling and learning. Our heterogeneous computer successfully performs probabilistic inference and asynchronous Boltzmann learning, despite device-to-device variations in sMTJs. A comprehensive comparison using a CMOS predictive process design kit (PDK) reveals that compact sMTJ-based p-bits replace 10,000 transistors while dissipating two orders of magnitude of less energy (2 fJ per random bit), compared to digital CMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnet approach can significantly advance probabilistic computing and its applications in various domains by providing massively parallel and truly random numbers with extremely high throughput and energy-efficiency

Dynamic twisting and imaging of moir\'e crystals

The electronic band structure is an intrinsic property of solid-state materials that is intimately connected to the crystalline arrangement of atoms. Moir\'e crystals, which emerge in twisted stacks of atomic layers, feature a band structure that can be continuously tuned by changing the twist angle between adjacent layers. This class of artificial materials blends the discrete nature of the moir\'e superlattice with intrinsic symmetries of the constituent materials, providing a versatile platform for investigation of correlated phenomena whose origins are rooted in the geometry of the superlattice, from insulating states at "magic angles" to flat bands in quasicrystals. Here we present a route to mechanically tune the twist angle of individual atomic layers with a precision of a fraction of a degree inside a scanning probe microscope, which enables continuous control of the electronic band structure in-situ. Using nanostructured rotor devices, we achieve the collective rotation of a single layer of atoms with minimal deformation of the crystalline lattice. In twisted bilayer graphene, we demonstrate nanoscale control of the moir\'e superlattice period via external rotations, as revealed using piezoresponse force microscopy. We also extend this methodology to create twistable boron nitride devices, which could enable dynamic control of the domain structure of moir\'e ferroelectrics. This approach provides a route for real-time manipulation of moir\'e materials, allowing for systematic exploration of the phase diagrams at multiple twist angles in a single device

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Do the High-Tech Industrial Development Zones Foster Urban Innovation? A Case Study of China

China’s high-tech industrial development zones (HIDZs) is a key “place-based” policy targeting national innovation-driven development. Despite the extensive research on HIDZs, it remains unclear whether and to what extent the policy has fostered cities’ innovation output and quality. Basing our research on panel data of Chinese cities from 2001 to 2019, we employed the PSM-DID model to examine the impact of HIDZs policy on their host cities’ innovation output and quality. The empirical results show that: (1) In general, the establishment of HIDZs has a more significant positive effect on fostering urban innovation output compared to its role in promoting urban innovation quality. However, (2) the effect on urban innovation output and quality varies across different cities. For cities with more advantageous locations and policies, HIDZ policy plays a more instrumental role in promoting the quality of urban innovation, while the establishment of HIDZs in other cities is more conducive to increasing the output of urban innovation. Ultimately, we argue that authorities must recognize the importance of integrated development of HIDZs and their host cities and incorporating HIDZs’ impact on the host cities into the HIDZ evaluation. It is necessary to understand that HIDZs have multiple development modes due to their specific local conditions. Hence, differentiated guidance must be carried out rather than directly replicating the experience from developed regions

Critical Signaling Transduction Pathways and Intestinal Barrier: Implications for Pathophysiology and Therapeutics

The intestinal barrier is a sum of the functions and structures consisting of the intestinal mucosal epithelium, mucus, intestinal flora, secretory immunoglobulins, and digestive juices. It is the first-line defense mechanism that resists nonspecific infections with powerful functions that include physical, endocrine, and immune defenses. Health and physiological homeostasis are greatly dependent on the sturdiness of the intestinal barrier shield, whose dysfunction can contribute to the progression of numerous types of intestinal diseases. Disorders of internal homeostasis may also induce barrier impairment and form vicious cycles during the response to diseases. Therefore, the identification of the underlying mechanisms involved in intestinal barrier function and the development of effective drugs targeting its damage have become popular research topics. Evidence has shown that multiple signaling pathways and corresponding critical molecules are extensively involved in the regulation of the barrier pathophysiological state. Ectopic expression or activation of signaling pathways plays an essential role in the process of shield destruction. Although some drugs, such as molecular or signaling inhibitors, are currently used for the treatment of intestinal diseases, their efficacy cannot meet current medical requirements. In this review, we summarize the current achievements in research on the relationships between the intestinal barrier and signaling pathways. The limitations and future perspectives are also discussed to provide new horizons for targeted therapies for restoring intestinal barrier function that have translational potential

Enhancing the Thermal Stability of Glutathione Bifunctional Synthase by B-Factor Strategy and Un/Folding Free Energy Calculation

Glutathione is of great significance in pharmaceutical and health fields, and one-step synthesis of reduced glutathione by glutathione bifunctional synthase has become a focus of research. The stability of glutathione bifunctional synthase is generally poor and urgently needs to be modified. The B-factor strategy and un/folding free energy calculation were both applied to enhance the thermal stability of glutathione bifunctional synthase from Streptococcus agalactiae (GshFSA). Based on the concept of B-factor strategy, we calculated the B-factor by molecular dynamics simulation to find flexible residues, performed point saturation mutations and high-throughput screening. At the same time, we also calculated the un/folding free energy of GshFSA and performed the point mutations. The optimal mutant from the B-factor strategy was R270S, which had a 2.62-fold increase in half-life period compared to the wild type, and the Q406M was the optimal mutant from the un/folding free energy calculation, with a 3.02-fold increase in half-life period. Both of them have provided a mechanistic explanation

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning.

Extending Moores law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency

Real-time Manipulation of Liquid Droplets using Photo-responsive Surfactant

Fast and programmable transport of liquid droplets on a solid substrate is desirable in microfluidic, thermal, biomedical, and energy devices. Past research has focused on designing substrates with asymmetric structures or gradient wettability where droplet behaviors are passively controlled, or by applying external electric, thermal, magnetic, or acoustic stimuli that either require the fabrication of electrodes or a strong applied field. In this work, we demonstrate tunable and programmable droplet motion on liquid-infused surfaces (LIS) and inside solid-surface capillary channels using low-intensity light and photo-responsive surfactants. When illuminated by the light of appropriate wavelengths, the surfactants can reversibly change their molecular conformation thereby tuning interfacial tensions in a multi-phase fluid system. This generates a Marangoni flow that drives droplet motions. With two novel surfactants that we synthesized, we demonstrate fast linear and complex 2D movements of droplets on liquid surfaces, on LIS, and inside microchannels. We also visualized the internal flow pattern using tracer particles and developed simple scaling arguments to explain droplet-size-dependent velocity. The method demonstrated in this study serves as a simple and exciting new approach for the dynamic manipulation of droplets for microfluidic, thermal, and water harvesting devices

Improving the Stability and Size Tunability of Cesium Lead Halide Perovskite Nanocrystals Using Trioctylphosphine Oxide as the Capping Ligand

Recently, all-inorganic cesium lead halide (CsPbX₃, X = Cl, Br, and I) nanocrystals (NCs) have drawn wide attention because of their excellent optoelectronic properties and potential applications. However, one of the most significant challenges of such NCs is their low stability against protonic solvents. In this work, we demonstrate that by incorporating a highly branched capping ligand, trioctylphosphine oxide (TOPO), into the traditional oleic acid/oleylamine system, monodisperse CsPbX₃ NCs with excellent optoelectronic properties can be achieved at elevated temperatures (up to 260 °C). The size of such NCs can be varied in a relatively wide range. The capping of TOPO on NCs has been verified through Fourier transform infrared spectroscopy measurement. More importantly, the presence of TOPO can dramatically improve the stability of CsPbX₃ NCs against ethanol treatment. After ethanol treatment for 100 min, the emission intensity of the TOPO-capped sample dropped only 5%, whereas that of non-TOPO-capped NCs dropped up to 86%. This work may shed some light on the preparation and application of CsPbX₃ NCs with higher stability