SerDes channel crosstalk mitigation methodology with industrial implementation guidance

With the increasing data rate of digital circuits, the differential crosstalk degrades the signal integrity performance in PCBs drastically. Usually, in the trace area, crosstalk can be isolated by stitching vias and adding shielding ground vias can also shield coupling in the ball gate array (BGA) and pin field area. The traditional way to mitigation crosstalk in the BGA and pin field area through adding more ground vias between signal pairs or increasing the spacing in between, it demonstrated us the efficiency on crosstalk cancellation efficiency but it also increases the size of products and it would be contradictory to the trend of the industry and market. The design of new channels with far less crosstalk but maintained or increased space efficiency is necessary. The proposed pin patterns in this research mitigate the differential crosstalk dramatically, yet maintained or even increased the signal vias to ground vias ratio (S:G). S:G is the ratio of signal vias to ground pins in a specific area of PCBs, it can represent the space efficiency). Crosstalk cancellated by using the principle of symmetry on two adjacent differential signal pairs in the BGA and pin field region. Except for the pin patterns, corresponding trace routing for the advance patterns also been researched and designed to maintain the crosstalk cancellation benefits in the pin field area. After all, interconnections between the chip package and the newly designed PCB have been studied and verified regarding industry capability and reliability. This research proposed for the SerDes channel and the validating is under the SerDes operating circumstance --Abstract, page iii

JTRF Volume 56 No. 3, Fall 2017Private Vehicle Ownership in Provincial China

Private vehicle ownership has rapidly grown with China’s economic development and increasing incomes. This paper analyzes China’s provincial demands for private vehicles during the post-opening period 2000 – 2012. Based on estimates from pooled, fi xed effects and Hausman-Taylor models, private vehicle ownership during this period grew at an average annual rate of over 20%, all else constant. The study focuses on the roles of economic, spatial, investment and regulatory factors in shaping private vehicle demands. The study fi nds that increases in GDP per capita and vehicle use cost reinforce and constrain, respectively, the strong trend toward increased ownership. And absent changes in population density, higher percentages of the population in urban areas increase the demand for private vehicles. But increasing population density provides stronger incentives for reducing vehicle demands. Municipal restrictions aimed at reducing the congestion and environmental effects of vehicle ownership and use are effective in reducing provincial demands. A separate analysis of provinces that are at least 60% urbanized identifi es important differences. Vehicle demands are income elastic and infrastructure investments have stronger effects in the most urbanized provinces than in less urbanized provinces

Can the Black Lives Matter Movement Reduce Racial Disparities? Evidence from Medical Crowdfunding

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM activities were most impacted. We construct innovative instrumental variable approaches that utilize weekends and rainfall to identify the global and local effects of BLM protests. Results suggest a broad social movement has a greater influence on charitable-giving behavior than a local event. Social media significantly magnifies the impact of protests

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. The core idea of our Fast-ACV is volume attention propagation (VAP) which can automatically select accurate correlation values from an upsampled correlation volume and propagate these accurate values to the surroundings pixels with ambiguous correlation clues. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve the state-of-the-art performance on several benchmarks (i.e., our ACVNet ranks the 2nd on KITTI 2015 and Scene Flow, and the 3rd on KITTI 2012 and ETH3D among all the published methods; our Fast-ACVNet outperforms almost all state-of-the-art real-time methods on Scene Flow, KITTI 2012 and 2015 and meanwhile has better generalization ability)Comment: Accepted to TPAMI 2023. arXiv admin note: substantial text overlap with arXiv:2203.0214

Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge, we propose a single-view and multi-view fused depth estimation system, which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth estimations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus, the system tends to choose the more reliable branch when facing textureless scenes, inaccurate calibration, dynamic objects, and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore, we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/AFNet/.Comment: Accepted to CVPR 202

The cooling intensity dependent on landscape complexity of green infrastructure in the metropolitan area

The cooling effect of green infrastructure (GI) is becoming a hot topic on mitigating the urban heat island (UHI) effect. Alterations to the green space are a viable solution for reducing land surface temperature (LST), yet few studies provide specific guidance for landscape planning adapted to the different regions. This paper proposed and defined the landscape complexity and the threshold value of cooling effect (TVoE). Results find that: (1) GI provides a better cooling effect in the densely built-up area than the green belt; (2) GI with a simple form, aggregated configuration, and low patch density had a better cooling intensity; (3) In the densely built-up area, TVoE of the forest area is 4.5 ha, while in the green belt, TVoE of the forest and grassland area is 9 ha and 2.25 ha. These conclusions will help the planners to reduce LST effectively, and employ environmentally sustainable planning

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis

Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence.Comment: Accepted by AAAI 2023 (Main Track), 9 pages, 4 figure

UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?

This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets