Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors with the UDF to the multi-view ground truth. However, these differentiable renderers are mainly handcrafted, which makes them either biased on ray-surface intersections, or sensitive to unsigned distance outliers, or not scalable to large scenes. To resolve these issues, we present a novel differentiable renderer to infer UDFs more accurately. Instead of using handcrafted equations, our differentiable renderer is a neural network which is pre-trained in a data-driven manner. It learns how to render unsigned distances into depth images, leading to a prior knowledge, dubbed volume rendering priors. To infer a UDF for an unseen scene from multiple RGB images, we generalize the learned volume rendering priors to map inferred unsigned distances in alpha blending for RGB image rendering. To reduce the bias of sampling in UDF inference, we utilize an auxiliary point sampling prior as an indicator of ray-surface intersection, and propose novel schemes towards more accurate and uniform sampling near the zero-level sets. We also propose a new strategy that leverages our pretrained volume rendering prior to serve as a general surface refiner, which can be integrated with various Gaussian reconstruction methods to optimize the Gaussian distributions and refine geometric details. Our results show that the learned volume rendering prior is unbiased, robust, scalable, 3D aware, and more importantly, easy to learn. Further experiments show that the volume rendering prior is also a general strategy to enhance other neural implicit representations such as signed distance function and occupancy. We evaluate our method on both widely used benchmarks and real scenes, and report superior performance over the state-of-the-art methods.

Portable volumetric video capturing systems present a compelling alternative to traditional, bulky prototype systems used for streaming and interacting with volumetric content. Their key advantages, particularly flexibility and ease of deployment, make them suitable for a wide range of applications. However, despite their potential, there has been limited exploration into the design of such portable systems. This paper addresses this gap by conducting an in-depth analysis of portability and proposing an optimized camera array configuration tailored for high-quality volumetric content generation. Our approach begins with a novel, flexible camera calibration method that leverages geometric priors, enabling accurate alignment of multiple cameras without requiring specialized expertise. Building on this, we introduce a meticulous fusion technique that integrates captured and inferred data to reconstruct complete volumetric representations. This method achieves a fusion latency of less than 100 ms, ensuring real-time performance. We integrate these innovations into a portable capturing system, named PortaCap, which incorporates a carefully designed camera deployment strategy. Through both quantitative and qualitative evaluations, PortaCap demonstrates significant improvements in volumetric content quality, achieving enhancements ranging from 13% to 33.8%. These results underscore the system's potential to advance the state-of-the-art in portable volumetric video capture.

This paper presents Hoyan-- the first reported large scale deployment of configuration verification in a global-scale wide area network (WAN). Hoyan has been running in production for more than two years and is currently used for all critical configuration auditing and updates on the WAN. We highlight our innovative designs and real-life experience to make Hoyan accurate and scalable in practice. For accuracy under the inconsistencies of devices' vendor-specific behaviors (VSBs), Hoyan continuously discovers the flaws in device behavior models, thus aiding the operators in fixing the models. For scalability to verify our global WAN, Hoyan introduces a "global-simulation & local formal-modeling" strategy to model uncertainties in small scales and perform aggressive pruning of possibilities during the protocol simulations. Hoyan achieves near-100% verification accuracy after it detected and fixed O(10) VSBs on our WAN. Hoyan has prevented many potential service failures resulting from misconfiguration and reduced the failure rate of updates of our WAN by more than half in 2019.

In-network Access Control List (ACL) is an important technique in ensuring network-wide connectivity and security. As cloud-scale WANs today constantly evolve in size and complexity, in-network ACL rules are becoming increasingly more complex. This presents a great challenge to the updating process of ACL configurations: network operators are frequently required to update "tangled" ACL rules across thousands of devices to meet diverse business requirements, and even a single ACL misconfiguration may lead to network disruptions. Such increasing challenges call for an automated system to improve the efficiency and correctness of ACL updates. This paper presents Jinjing, a system that aids Alibaba's network operators in automatically and correctly updating ACL configurations in Alibaba's global WAN. Jinjing allows the operators to express in a declarative language, named LAI, their update intent (e.g., ACL migration and traffic control). Then, Jinjing automatically synthesizes ACL update plans that satisfy their intent. At the heart of Jinjing, we develop a set of novel verification and synthesis techniques to rigorously guarantee the correctness of update plans. In Alibaba, our operators have used Jinjing to efficiently update their ACLs and have thus prevented significant service downtime.

A novel monitoring strategy is proposed for multimode process in which mode clustering and fault detection based on geodesic distance (GD) are integrated. To start with, the empowered adjacency matrix of normalized training dataset is obtained and improved Dijkstra algorithm (IDA) is utilized to calculate the geodesic distance between each sample data so as to characterize the shortest distance of the nonlinear data within local areas accurately. Next, GD matrix algorithm is presented as an optimal clustering solution for a multimode process dataset. Then, the GDS model is established in each operating mode. Monitoring statistics based on the power of geodesic distance are structured based on square sum of Euclidean distances. Once the test data is detected as fault data, mode location based on deviation coefficient is conducted to narrow the scope of the inspection fault. Finally, the validity and usefulness of the proposed GDMPM monitoring method are demonstrated through the Tennessee Eastman (TE) benchmark process.

Radio-frequency (RF) transceiver array design using primary and higher order harmonics for in vivo parallel magnetic resonance imaging imaging (MRI) and spectroscopic imaging is proposed. The improved electromagnetic decoupling performance, unique magnetic field distributions and high-frequency operation capabilities of higher-order harmonics of resonators would benefit transceiver arrays for parallel MRI, especially for ultrahigh field parallel MRI. To demonstrate this technique, microstrip transceiver arrays using first and second harmonic resonators were developed for human head parallel imaging at 7T. Phantom and human head images were acquired and evaluated using the GRAPPA reconstruction algorithm. The higher-order harmonic transceiver array design technique was also assessed numerically using FDTD simulation. Compared with regular primary-resonance transceiver designs, the proposed higher-order harmonic technique provided an improved g-factor and increased decoupling among resonant elements without using dedicated decoupling circuits, which would potentially lead to a better parallel imaging performance and ultimately faster and higher quality imaging. The proposed technique is particularly suitable for densely spaced transceiver array design where the increased mutual inductance among the elements becomes problematic. In addition, it also provides a simple approach to readily upgrade the channels of a conventional primary resonator microstrip array to a larger number for faster imaging.

Double-tuned radio-frequency (RF) coils for heteronuclear mangentic resonance (MR) require sufficient electromagnetic isolation between the two resonators operating at two Larmor frequencies and independent tuning in order to attain highly efficient signal acquisition at each frequency. In this work, a novel method for double-tuned coil design at 7T based on the concept of common-mode differential-mode (CMDM) was developed and tested. Common mode (CM) and differential mode (DM) currents exist within two coupled parallel transmission lines, e.g., microstrip lines, yielding two different current distributions. The electromagnetic (EM) fields of the CM and DM are orthogonal to each other, and thus, the two modes are intrinsically EM decoupled. The modes can be tuned independently to desired frequencies, thus satisfying the requirement of dual-frequency MR applications. To demonstrate the feasibility and efficiency of the proposed CMDM technique, CMDM surface coils and volume coils using microstrip transmission line for (1)H and (13)C MRI/MRSI were designed, constructed, and tested at 7T. Bench test results showed that the isolations between the two frequency channels of the CMDM surface coil and volume coil were better than -30 and -25 dB, respectively. High quality MR phantom images were also obtained using the CMDM coils. The performance of the CMDM technique was validated through a comparison with the conventional two-pole design method at 7T. The proposed CMDM technique can be also implemented by using other coil techniques such as lumped element method, and can be applied to designing double-tuned parallel imaging coil arrays. Furthermore, if the two resonant modes of a CMDM coil were tuned to the same frequency, the CMDM coil becomes a quadrature coil due to the intrinsic orthogonal field distribution of CM and DM.

The high-frequency transceiver array based on the microstrip transmission line design is a promising technique for ultrahigh field magnetic resonance imaging (MRI) signal excitation and reception. However, with the increase of radio-frequency (RF) channels, the size of the ground plane in each microstrip coil element is usually not sufficient to provide a perfect ground. Consequently, the transceiver array may suffer from cable resonance, lower Q-factors, and imaging quality degradations. In this paper, we present an approach to improving the performance of microstrip transceiver arrays by introducing RF shielding outside the microstrip array and the feeding coaxial cables. This improvement reduced interactions among cables, increased resonance stability, and Q-factors, and thus improved imaging quality. An experimental method was also introduced and utilized for quantitative measurement and evaluation of RF coil resonance stability or "cable resonance" behavior.