CryoFormer: Continuous Reconstruction of 3D Structures from Cryo-EM Data using Transformer-based Neural Representations

High-resolution heterogeneous reconstruction of 3D structures of proteins and other biomolecules using cryo-electron microscopy (cryo-EM) is essential for understanding fundamental processes of life. However, it is still challenging to reconstruct the continuous motions of 3D structures from hundreds of thousands of noisy and randomly oriented 2D cryo-EM images. Existing methods based on coordinate-based neural networks show compelling results to model continuous conformations of 3D structures in the Fourier domain, but they suffer from a limited ability to model local flexible regions and lack interpretability. We propose a novel approach, cryoFormer, that utilizes a transformer-based network architecture for continuous heterogeneous cryo-EM reconstruction. We for the first time directly reconstruct continuous conformations of 3D structures using an implicit feature volume in the 3D spatial domain. A novel deformation transformer decoder further improves reconstruction quality and, more importantly, locates and robustly tackles flexible 3D regions caused by conformations. In experiments, our method outperforms current approaches on three public datasets (1 synthetic and 2 experimental) and a new synthetic dataset of PEDV spike protein. The code and new synthetic dataset will be released for better reproducibility of our results. Project page: https://cryoformer.github.io

Metawaveguide for Asymmetric Interferometric Light-Light Switching

Light-light switching typically requires strong nonlinearity where intense laser fields route and direct data flows of weak power, leading to a high power consumption that limits its practical use. Here we report an experimental demonstration of a metawaveguide that operates exactly in the opposite way in a linear regime, where an intense laser field is interferometrically manipulated on demand by a weak control beam with a modulation extinction ratio up to approximately 60 dB. This asymmetric control results from operating near an exceptional point of the scattering matrix, which gives rise to intrinsic asymmetric reflections of the metawaveguide through delicate interplay between index and absorption. The designed metawaveguide promises low-power interferometric light-light switching for the next generation of optical devices and networks

Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions due to malfunction or adversarial attack. To address the uncertainty that any agent can be adversarial, we propose a Bayesian Adversarial Robust Dec-POMDP (BARDec-POMDP) framework, which views Byzantine adversaries as nature-dictated types, represented by a separate transition. This allows agents to learn policies grounded on their posterior beliefs about the type of other agents, fostering collaboration with identified allies and minimizing vulnerability to adversarial manipulation. We define the optimal solution to the BARDec-POMDP as an ex post robust Bayesian Markov perfect equilibrium, which we proof to exist and weakly dominates the equilibrium of previous robust MARL approaches. To realize this equilibrium, we put forward a two-timescale actor-critic algorithm with almost sure convergence under specific conditions. Experimentation on matrix games, level-based foraging and StarCraft II indicate that, even under worst-case perturbations, our method successfully acquires intricate micromanagement skills and adaptively aligns with allies, demonstrating resilience against non-oblivious adversaries, random allies, observation-based attacks, and transfer-based attacks

Human Performance Modeling and Rendering via Neural Animated Mesh

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.Comment: 18 pages, 17 figure

Software-Defined Networking Control for X-haul Optical Networks in Testbed Experiments and Emulation

Today’s telecommunication networks encounter challenges with rapidly growing traffic demands in various internet applications and services, such as video streaming, augmented/virtual reality, connected vehicles, dense wireless radio nodes, and edge cloud computing. Cloud radio access networks (C-RANs) have been proposed to enable resource sharing, modular radio functions, network scalability, and efficient energy management for future mobile wireless networks. In C-RANs, traditional co-located baseband units (BBUs) and radio units (RUs) are split into central units (CU) hosting BBU pools and massive numbers of RUs connected through fronthaul (FH) optical transport links. However, the communication between CUs and RUs using either digital transmission with the common public radio interface (CPRI) or analog transmission with radio-over-fiber requires high bandwidth and strict synchronization delay limits. Thus, the evolution of next-generation optical transport systems is required to build efficient, dynamic, and scalable communication networks that support data transmission with high capacity and ultra-low latency to realize high performing C-RAN architectures. Conventional commercial optical transport systems in metropolitan areas are based on wavelength-division multiplexing (WDM) networks where static wavelength channels are provisioned along fiber links between network nodes (containing optical switches or amplifiers) to ensure the data transmission of the peak traffic for backhaul (BH). This results in inefficient utilization of optical network resources in C-RANs where high-capacity and low-latency x-haul (FH, midhaul, and BH) optical transport is required. In addition, conventional optical network elements (NEs) with vendor-specific operating systems (OS) increases the cost of upgrading the system for higher performance, and the complexity of designing novel control planes for scalable networks. To address these problems, there is growing interest in optical transport networks built with open and fully-programmable optical systems using software defined networking (SDN) controlled white-boxes such as reconfigurable add/drop multiplexers (ROADMs), optical circuit switches (OCSs), and erbium dopped fiber amplifiers (EDFAs). This thesis examines SDN control strategies for x-haul optical systems in 5G and beyond wireless radio access networks. First, the Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) advanced wireless testbed is reviewed. A dedicated multi-functional Ryu SDN controller is implemented in the testbed’s optical network with wavelength channel assignment and topology reconfiguration for intra-/inter- domain control, network element (NE) monitoring, and a wireless handover experiment. Secondly, a BBU pool allocation optimization algorithm and a physical impairment-aware routing and wavelength assignment (PIA-RWA) considering midhaul BBU-RU functional split are explored to maximize traffic capacity and minimize resource occupation in an optical network of a New York metropolitan area C-RANs use case. In addition, several artificial neural network (ANN) models are also investigated to contribute accurate quality of transmission (QoT) prediction tools of the physical optical layer. Lastly, Mininet-Optical is developed as an extension to Mininet to achieve a novel multi-layer network emulation tool for SDN controller development. A dynamic optical SDN controller with least-congested PIA-RWA and BBU resource load balancing strategies is evaluated to enhance the network capacity in a virtual COSMOS environment emulated by Mininet-Optical considering various diurnal wireless traffic patterns

Physical Layer Control for Disaggregated Optical Systems

Disaggregating optical communication systems can impact physical layer control. Recent progress on multi-domain transmission control and machine-learning provide capabilities for adaptation and development of engineering rules in the field with potential benefits for disaggregated systems.National Science Foundation [PFI-AIR-TT: 1601784, ECC: 0812072, CNS: 1650669]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Quantitative analyses of products and rates in polyethylene depolymerization and upcycling.

Depolymerization and upcycling are promising approaches to managing plastic waste. However, quantitative measurements of reaction rates and analyses of complex product mixtures arising from depolymerization of polyolefins constitute significant challenges in this emerging field. Here, we detail techniques for recovery and analysis of products arising from batch depolymerization of polyethylene. We also describe quantitative analyses of reaction rates and products selectivity. This protocol can be extended to depolymerization of other plastics and characterization of other product mixtures including long-chain olefins. For complete details on the use and execution of this protocol, please refer to Sun et al.1

Gradient Corner Pooling for Keypoint-Based Object Detection

Detecting objects as multiple keypoints is an important approach in the anchor-free object detection methods while corner pooling is an effective feature encoding method for corner positioning. The corners of the bounding box are located by summing the feature maps which are max-pooled in the x and y directions respectively by corner pooling. In the unidirectional max pooling operation, the features of the densely arranged objects of the same class are prone to occlusion. To this end, we propose a method named Gradient Corner Pooling. The spatial distance information of objects on the feature map is encoded during the unidirectional pooling process, which effectively alleviates the occlusion of the homogeneous object features. Further, the computational complexity of gradient corner pooling is the same as traditional corner pooling and hence it can be implemented efficiently. Gradient corner pooling obtains consistent improvements for various keypoint-based methods by directly replacing corner pooling. We verify the gradient corner pooling algorithm on the dataset and in real scenarios, respectively. The networks with gradient corner pooling located the corner points earlier in the training process and achieve an average accuracy improvement of 0.2%-1.6% on the MS-COCO dataset. The detectors with gradient corner pooling show better angle adaptability for arrayed objects in the actual scene test

Quantitative analyses of products and rates in polyethylene depolymerization and upcycling

Summary: Depolymerization and upcycling are promising approaches to managing plastic waste. However, quantitative measurements of reaction rates and analyses of complex product mixtures arising from depolymerization of polyolefins constitute significant challenges in this emerging field. Here, we detail techniques for recovery and analysis of products arising from batch depolymerization of polyethylene. We also describe quantitative analyses of reaction rates and products selectivity. This protocol can be extended to depolymerization of other plastics and characterization of other product mixtures including long-chain olefins.For complete details on the use and execution of this protocol, please refer to Sun et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics