ChatEDA: A Large Language Model Powered Autonomous Agent for EDA

The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities in natural language processing and comprehension, offering a novel approach to interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent for EDA empowered by a large language model, AutoMage, complemented by EDA tools serving as executors. ChatEDA streamlines the design flow from the Register-Transfer Level (RTL) to the Graphic Data System Version II (GDSII) by effectively managing task planning, script generation, and task execution. Through comprehensive experimental evaluations, ChatEDA has demonstrated its proficiency in handling diverse requirements, and our fine-tuned AutoMage model has exhibited superior performance compared to GPT-4 and other similar LLMs

PKU-DyMVHumans:A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios, each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background decomposition, high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Extensive studies are performed on the benchmark, demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data. The dataset is available at: https://pku-dymvhumans.github.io

PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios, each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background decomposition, high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Extensive studies are performed on the benchmark, demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data.Comment: CVPR2024(accepted). Project page: https://pku-dymvhumans.github.i

A novel distance learning for elastic cross-modal audio-visual matching

In this work we propose a novel network formulation for joint representation of cross-modal audio and visual information base on metric learning. We employ a distance learning framework as a training procedure. For this purpose we introduce an elastic matching network (EmNet) and a novel loss function to learn the shared latent space representation of multi-modal information. The elastic matching network is capable of matching given face image (or audio voice clip) from diverse number of audio clips (or face images). We quantitatively and qualitatively evaluate the purposed approach on the standard audio-visual matching evaluation dataset, the overlap of VoxCeleb and VGGFace by both multi-way and binary audio-visual matching tasks. The promising performance comparing to the existing methods verifies the effectiveness of the proposed approach, which yields to a new state-of-the-art for cross-modal audio-visual matching

Staged open reduction and internal fixation with double-locking plates to treat bilateral distal femur periprosthetic fractures after total knee arthroplasty: A case report

BackgroundThe incidence of periprosthetic fractures after total knee arthroplasty (TKA) increases in parallel with the number of procedures. Comminuted fractures along the primary fracture line extending to the edge of the prosthesis are challenging, and bilateral fractures are rarely reported, especially with open injuries.Case presentationA 65-year-old female had undergone bilateral TKA in our hospital 5 years before admission. She was admitted with a traumatic bilateral Rorabeck type II B distal femur periprosthetic fracture (closed right, open left, Gustilo II) and was treated with bilateral staged open reduction and internal fixation (ORIF) with double-locking plates. The patient experienced a prolonged delayed fracture union and finally healed around 21 months postoperatively. The function was satisfactory after 4 years of follow-up.ConclusionORIF with double-locking plates can be used to treat Rorabeck II B periprosthetic fracture where the primary fracture line extends beyond the edge of the prosthesis; however, there may be delayed healing or nonunion. Patients need to undergo long-term rehabilitation and endure long disability times and require good rehabilitation nursing care. Once they achieve bone healing, the treatment achieves bone preservation and substantial prosthesis survival

Quantum frequency conversion and single-photon detection with lithium niobate nanophotonic chips

In the past few years, the lithium niobate on insulator (LNOI) platform has revolutionized lithium niobate materials, and a series of quantum photonic chips based on LNOI have shown unprecedented performances. Quantum frequency conversion (QFC) photonic chips, which enable quantum state preservation during frequency tuning, are crucial in quantum technology. In this work, we demonstrate a low-noise QFC process on an LNOI nanophotonic platform designed to connect telecom and near-visible bands with sum-frequency generation by long-wavelength pumping. An internal conversion efficiency of 73% and an on-chip noise count rate of 900 counts per second (cps) are achieved. Moreover, the on-chip preservation of quantum statistical properties is verified, showing that the QFC chip is promising for extensive applications of LNOI integrated circuits in quantum information. Based on the QFC chip, we construct an upconversion single-photon detector with the sum-frequency output spectrally filtered and detected by a silicon single-photon avalanche photodiode, demonstrating the feasibility of an upconversion single-photon detector on-chip with a detection efficiency of 8.7% and a noise count rate of 300 cps. The realization of a low-noise QFC device paves the way for practical chip-scale QFC-based quantum systems in heterogeneous configurations.Comment: 8pages, 6 figures, 1 tabl

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

The Southern Ocean connects the Pacific, Atlantic, and Indian Oceans, serving as a key hub for the global overturning circulation. The climate of the Southern Ocean is closely linked to the low-latitude equatorial Pacific, as well as the high-latitude regions of the North Atlantic, making it an important component of the global climate system. Due to the interactions of various processes such as atmospheric, oceanic, and ice cover, the Southern Ocean exhibits a complex and variable sea surface temperature structure. Satellite observations indicate that since 1980, the sea surface temperature of the Southern Ocean has been cooling, contrary to the global warming trend. However, due to the relatively short length of satellite observations, the specific mechanisms are not yet clear. Here, we used the EOF method to analyze sea surface temperature data since 1870 (HadISST1 and ERSSTV5), with three main separated modes explaining over 70% of the sea temperature variability. Among them, the first mode shows widespread positive sea surface temperature anomalies in the Southern Ocean, with a time series change consistent with global temperature anomalies, representing a mode of global warming. The second mode corresponds to the Atlantic Multidecadal Oscillation (AMO) but with a lag of approximately 4 years. The third mode is consistent with the variability of the El Niño-Southern Oscillation (ENSO). Furthermore, our study indicates that despite the ongoing global warming since 1980, the negative phase of AMO and positive phase of ENSO may counteract the effects of global warming, leading to an overall cooling trend in the sea surface temperature of the Southern Ocean

Reduced cardioprotective action of adiponectin in high-fat diet-induced type II diabetic mice and its underlying mechanisms.

Diabetes exacerbates ischemic heart disease morbidity and mortality via incompletely understood mechanisms. Although adiponectin (APN) reduces myocardial ischemia/reperfusion (MI/R) injury in nondiabetic animals, whether APN\u27s cardioprotective actions are altered in diabetes, a pathologic condition with endogenously reduced APN, has never been investigated. High-fat diet (HD)-induced diabetic mice and normal diet (ND) controls were subjected to MI via coronary artery ligation, and given vehicle or APN globular domain (gAPN, 2 μg/g) 10 min before reperfusion. Compared to ND mice (where gAPN exerted pronounced cardioprotection), HD mice manifested greater MI/R injury, and a tripled gAPN dose was requisite to achieve cardioprotective extent seen in ND mice (i.e., infarct size, apoptosis, and cardiac function). APN reduces MI/R injury via AMP-activated protein kinase (AMPK)-dependent metabolic regulation and AMPK-independent antioxidative/antinitrative pathways. Compared to ND, HD mice manifested significantly blunted gAPN-induced AMPK activation, basally and after MI/R (p\u3c0.05). Although both low- and high-dose gAPN equally attenuated MI/R-induced oxidative stress (i.e., NADPH oxidase expression and superoxide production) and nitrative stress (i.e., inducible nitric oxide synthase expression, nitric oxide production, and peroxynitrite formation) in ND mice, only high-dose gAPN efficaciously did so in HD mice. We demonstrate for the first time that HD-induced diabetes diminished both AMPK-dependent and AMPK-independent APN cardioprotection, suggesting an unreported diabetic heart APN resistance

Nano-channel-based physical and chemical synergic regulation for dendrite-free lithium plating

Uncontrollable dendrite growth resulting from the non-uniform lithium ion (Li+) flux and volume expansion in lithium metal (Li) negative electrode leads to rapid performance degradation and serious safety problems of lithium metal batteries. Although N-containing functional groups in carbon materials are reported to be effective to homogenize the Li+ flux, the effective interaction distance between lithium ions and N-containing groups should be relatively small (down to nanometer scale) according to the Debye length law. Thus, it is necessary to carefully design the microstructure of N-containing carbon materials to make the most of their roles in regulating the Li+ flux. In this work, porous carbon nitride microspheres (PCNMs) with abundant nanopores have been synthesized and utilized to fabricate a uniform lithiophilic coating layer having hybrid pores of both the nano- and micrometer scales on the Cu/Li foil. Physically, the three-dimensional (3D) porous framework is favorable for absorbing volume changes and guiding Li growth. Chemically, this coating layer can render a suitable interaction distance to effectively homogenize the Li+ flux and contribute to establishing a robust and stable solid electrolyte interphase (SEI) layer with Li-F, Li-N, and Li-O-rich contents based on the Debye length law. Such a physical-chemical synergic regulation strategy using PCNMs can lead to dendrite-free Li plating, resulting in a low nucleation overpotential and stable Li plating/stripping cycling performance in both the Li‖Cu and the Li‖Li symmetric cells. Meanwhile, a full cell using the PCNM coated Li foil negative electrode and a LiFePO4 positive electrode has delivered a high capacity retention of ∼ 80% after more than 200 cycles at 1 C and achieved a remarkable rate capability. The pouch cell fabricated by pairing the PCNM coated Li foil negative electrode with a NCM 811 positive electrode has retained ∼ 73% of the initial capacity after 150 cycles at 0.2 C