3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V

In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals that while GPT-4V exhibits proficiency in discerning the position and interrelations of 2D entities through current visual prompting techniques, its abilities in handling 3D spatial tasks have yet to be explored. In our approach, we create a 3D coordinate system tailored to 3D imagery, complete with annotated scale information. By presenting images infused with the 3DAP visual prompt as inputs, we empower GPT-4V to ascertain the spatial positioning information of the given 3D target image with a high degree of precision. Through experiments, We identified three tasks that could be stably completed using the 3DAP method, namely, 2D to 3D Point Reconstruction, 2D to 3D point matching, and 3D Object Detection. We perform experiments on our proposed dataset 3DAP-Data, the results from these experiments validate the efficacy of 3DAP-enhanced GPT-4V inputs, marking a significant stride in 3D spatial task execution

LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark

Large language models have become a potential pathway toward achieving artificial general intelligence. Recent works on multi-modal large language models have demonstrated their effectiveness in handling visual modalities. In this work, we extend the research of MLLMs to point clouds and present the LAMM-Dataset and LAMM-Benchmark for 2D image and 3D point cloud understanding. We also establish an extensible framework to facilitate the extension of MLLMs to additional modalities. Our main contribution is three-fold: 1) We present the LAMM-Dataset and LAMM-Benchmark, which cover almost all high-level vision tasks for 2D and 3D vision. Extensive experiments validate the effectiveness of our dataset and benchmark. 2) We demonstrate the detailed methods of constructing instruction-tuning datasets and benchmarks for MLLMs, which will enable future research on MLLMs to scale up and extend to other domains, tasks, and modalities faster. 3) We provide a primary but potential MLLM training framework optimized for modalities' extension. We also provide baseline models, comprehensive experimental observations, and analysis to accelerate future research. Codes and datasets are now available at https://github.com/OpenLAMM/LAMM.Comment: 37 pages, 33 figures. Code available at https://github.com/OpenLAMM/LAMM ; Project page: https://openlamm.github.io

A new skew Laplace distribution(一种非对称拉普拉斯分布)

讨论一种新的非对称拉普拉斯分布，研究了该分布的性质、数字特征、参数估计、应用等，并将该分布与拉普拉斯分布在实际应用中的效果进行了对比

New Insight of Maximum Transferred Power by Matching Capacitance of a Wireless Power Transfer System

Most research on wireless power transfer (WPT) has been focused on how to achieve a high-efficiency power transfer. Our study found that under the impedance matching for achieving maximum WPT efficiency, the power transferred to the load cannot reach the maximum when a WPT system is supplied by an AC voltage source with constant amplitude. However, the load power or the voltage across the load is essential for a low-power electric device such as the implanted medical device where the transfer efficiency is not the priority to be considered. The paper presents a method for achieving maximum power on the load by matching capacitance in a WPT system with given two-coupled-coils. Three sets of matching capacitances for extreme load power were deduced based on the circuit model considering the coil\u27s resistance, and all these three matching make the WPT system operate at the resonant state. Two sets can make the system achieve the global maximum of load power. One set can make the system achieve the local maximum of load power and reach the power transfer efficiency next to 1. Experimental results verified the theoretical calculations. The results can contribute to the compensation design of a practical WPT system for transferring the maximum power to the load

A transient production prediction method for tight condensate gas wells with multiphase flow

Considering the phase behaviors in condensate gas reservoirs and the oil-gas two-phase linear flow and boundary-dominated flow in the reservoir, a method for predicting the relationship between oil saturation and pressure in the full-path of tight condensate gas well is proposed, and a model for predicting the transient production from tight condensate gas wells with multiphase flow is established. The research indicates that the relationship curve between condensate oil saturation and pressure is crucial for calculating the pseudo-pressure. In the early stage of production or in areas far from the wellbore with high reservoir pressure, the condensate oil saturation can be calculated using early-stage production dynamic data through material balance models. In the late stage of production or in areas close to the wellbore with low reservoir pressure, the condensate oil saturation can be calculated using the data of constant composition expansion test. In the middle stages of production or when reservoir pressure is at an intermediate level, the data obtained from the previous two stages can be interpolated to form a complete full-path relationship curve between oil saturation and pressure. Through simulation and field application, the new method is verified to be reliable and practical. It can be applied for prediction of middle-stage and late-stage production of tight condensate gas wells and assessment of single-well recoverable reserves

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15–45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth

Temporal and Spatial Changes in Vegetation Ecological Quality and Driving Mechanism in Kökyar Project Area from 2000 to 2021

The “Kökyar Greening Project” in the suburb of Aksu, Xinjiang, is a model of large-area artificial afforestation in an environment of drought and water scarcity. As an important part of the “3-North Shelter Forest Program”, it plays an important role in promoting the economic development and the environmentally friendly construction of Aksu and even of the whole Xinjiang region. Based on multisource remote-sensing data and meteorological observation data, this study explored the temporal and spatial changes in the vegetation parameters (FVC, NPP, and VEQI) and the ecological parameters (RSEI and LULC) in the Kökyar Project Area from 2000 to 2021. Based on the Theil–Sen median and TSS-RESTREND, this study investigated the path of mutual influence among the FVC, NPP, VEQI, and RSEI, as well as their responses to climate change and human activities. The results show that: (1) from 2000 to 2021, the FVC, NPP, VEQI, and RSEI in the Kökyar Project Area showed a significant upward trend and showed the distribution characteristics of “high in the south and low in the north”. (2) Over the past 22 years, the RSEI has shown a significant increase with the FVC, NPP and VEQI (p < 0.001), indicating that the “Kökyar Greening Project” has achieved significant ecological benefits. (3) The changes in the vegetation parameters and RSEI in the Kökyar Project Area were dominated by human activities. (4) The Kökyar Project Area has caused great changes to the ecosystem pattern of the region, and the vegetation parameters and RSEI in the Kökyar Project Area have increased, mainly in the form of cropland and grassland expansion over the past 22 years

Labyrinthin Expression Is Associated with Poor Prognosis in Patients with Non-Small-Cell Lung Cancer.

To determine Labyrinthin (LAB) expression in non-small-cell lung cancer (NSCLC), we immunostained and scored for LAB immunohistochemistry (IHC) expression on sections of tissue microarrays (TMAs) prepared from 256 archival tissue blocks of NSCLC. Propensity-score-weighted Kaplan-Meier curves and weighted Cox models were used to associate LAB expression with overall survival. LAB mRNA expression was assessed in The Cancer Genome Atlas (TCGA) and correlated with clinical phenotype and outcome. Positive LAB IHC expression (&gt;5% of tumor cells) was detected in 208/256 (81.3%) of NSCLC samples, and found in both lung adenocarcinomas (LUAD) and lung squamous cell cancer (LUSC). LAB positivity was associated with poor overall survival (HR = 3.56, 95% CI: 2.3-5.4; p &lt; 0.0001) and high tumor differentiation grade or metastasis compared with negative LAB expression. Univariant and multivariate survival analyses demonstrated LAB expression as an independent prognostic factor for NSCLC patients. LAB RNA expression in TCGA-LUAD was higher in primary and advanced-stage tumors than in normal tissue, and was associated with poorer overall survival. No significant differences or associations were found with LAB RNA expression in TCGA-LUSC. The LAB IHC assay is being used to identify candidate cancer patients for the first-in-human phase I trial evaluating the LAB vaccines (UCDCC#296, NCT051013560)