Hidden topic–emotion transition model for multi-level social emotion detection

With the fast development of online social platforms, social emotion detection, focusing on predicting readers’ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs. Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity. However, the analysis of GNN performance with respect to nodes exhibiting different structural patterns, e.g., homophilic nodes in heterophilic graphs, remains rather limited. In the present study, we provide evidence that Graph Neural Networks(GNNs) on node classification typically perform admirably on homophilic nodes within homophilic graphs and heterophilic nodes within heterophilic graphs while struggling on the opposite node set, exhibiting a performance disparity. We theoretically and empirically identify effects of GNNs on testing nodes exhibiting distinct structural patterns. We then propose a rigorous, non-i.i.d PAC-Bayesian generalization bound for GNNs, revealing reasons for the performance disparity, namely the aggregated feature distance and homophily ratio difference between training and testing nodes. Furthermore, we demonstrate the practical implications of our new findings via (1) elucidating the effectiveness of deeper GNNs; and (2) revealing an over-looked distribution shift factor on graph out-of-distribution problem and proposing a new scenario accordingly.Comment: 54 pages, 24 figure

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

A time–frequency analysis based internal leakage detection method for hydraulic actuators

Internal leakage in the hydraulic actuators is concerned in this article, which is caused by seal damage, resulting in the limited performance of the system. To study the issue, this article proposes a method based on time–frequency analysis for the detection in hydraulic actuators. First, the pressure signal after filtering of the actuator in one side is collected when the control valve is affected by sinusoidal-like inputs. Second, the time–frequency image of pressure signal in a period at different leakage levels is obtained after continuous wavelet transform. Third is the sum of pixels in the time–frequency image. It is shown that the feature pattern is established by the sum of pixels in the time–frequency image that internal leakage and its severity could be detected effectively. The proposed method required two baselines and premeasured the pressure signal at 11 leakage levels. Once the sum of pixels in the time–frequency image values, obtained from the time–frequency image by continuous wavelet transform based on wavelet Cmor1-1 in subsequent offline tests, are greater than the first baseline, a leakage alarm is triggered. Furthermore, a severe leakage alarm is triggered when the value is greater than the second baseline. Experimental tests show the accuracy of the proposed scheme at different mother wavelets, and it is done without knowing the model of actuator or leakage

Influence of AlN Passivation on Dynamic ON-Resistance and Electric Field Distribution in High-Voltage AlGaN/GaN-on-Si HEMTs

We investigate in detail the influence of AlN passivation on dynamic ON-resistance (R-ON) and electric field (E-field) distribution in high-voltage AlGaN/GaN high electron mobility transistors (HEMTs) on a Si substrate based on pulsed I-V measurements, electroluminescence (EL) microscopy, and 2-D physics-based numerical device simulations. It is found that the dynamic R-ON increase has been significantly suppressed to below 10% at various temperatures ranging from -50 to 200 degrees C owing to the effective and robust compensation of deep acceptor-like trap states at the AlN/GaN (passivation/cap) interface by the additional positive polarization charges induced in the epitaxial AlN thin passivation layer grown in a plasma-enhanced atomic layer deposition system. To the best of our knowledge, this is the first time that highly suppressed current collapse in an AlGaN/GaN HEMT on a Si substrate within a wide temperature range is ever reported. By monitoring the dynamic R-ON for 100 consecutive 133-kHz switching cycles, its variation is observed to be less than 2.5%, indicating excellent stability of the passivation effectiveness. The electric field in an AlN-passivated device is found to be well confined at the drain-side gate edge, as shown in EL measurement and numerical simulation results. This phenomenon directly suggests that the virtual gate effect arising from surface trap charging has been effectively alleviated by the AlN passivation technique

Stability and temperature dependence of dynamic RON in AlN-passivated AlGaN/GaN HEMT on Si substrate

We carried out detailed characterization and evaluation of dynamic performance of high-voltage AlGaN/GaN high electron mobility transistors (HEMTs) with AlN/SiNx passivation by means of pulsed I-V measurements. Transient OFF-to-ON switching tests verify the effectiveness of surface passivation by PE-ALD grown AlN epitaxial layer. The dynamic ON-resistance (RON) measured 350 ns after the switching event (500 ns) remains as low as only 1.08 times the static RON with an OFF-state drain bias of 60 V. Less than 10% degradation in dynamic RON is achieved under 40-V switching at various frequencies of 1-133 kHz within a wide temperature range of.50-200 °C. The stability of dynamic RON is also confirmed with a simple approach by monitoring the pulsed current at a drain bias of ∼1 V for 100 consecutive switching cycles

Impact of Mulching on Soil Moisture and Sap Flow Characteristics of Jujube Trees

The main purpose of this study was to assess the influence of grass planting and jujube branch mulching on soil moisture levels and jujube tree transpiration rates, with the ultimate goal of improving jujube tree production in rain-fed orchards. The study encompassed four treatments: jujube branch mulching (JBM), jujube branch mulching with white clover planting (JBM + WCP), white clover planting (WCP), and clean cultivation (CC). During a two-year experiment, it was observed that the JBM treatment exhibited the highest capacity for moisture conservation. Specifically, it resulted in an average increase of 2.69% (in 2013) and 2.23% (in 2014) in soil moisture content compared with the CC treatment. The application of statistical analysis revealed significant differences (p p p p p < 0.05) compared with JBM + WCP. The sap flow velocity was positively correlated with air temperature, vapor pressure deficit, wind velocity, photosynthetically active radiation, and soil temperature. Photosynthetically active radiation was identified as the main driving factor influencing jujube tree transpiration. In conclusion, the findings of this study demonstrate the effectiveness of using pruned jujube branches for coverage in rain-fed jujube orchards. This approach not only conserves mulching materials and diminishes the expenses associated with transporting pruned jujube tree branches away from the jujube orchard but also achieves multiple objectives, including increasing soil moisture, promoting jujube tree transpiration, and enhancing soil water utilization. These results have significant implications for the efficient utilization of rainwater resources in rain-fed jujube orchards and provide valuable insights for practical applications in orchard management

Dynamic evaluation method of water-sealed gas for ultra-deep buried fractured tight gas reservoir in Kuqa Depression, Tarim Basin, China

The ultra-deep-buried fractured tight gas reservoir in the Kuqa Depression of the Tarim Basin has developed edge and bottom water. Faults and fractures have become “highways” for water invasion, resulting in the “water sealed gas” effect and reducing gas reservoir recovery. At present, there is a lack of effective evaluation methods. Therefore, based on an analysis of water invasion characteristics of the gas reservoir, a dynamic evaluation method for water-sealed gas in a fractured gas reservoir is established. This method considers two factors: fracture development scale and peripheral water body strength. It is then applied to three developed blocks in the Kuqa ultra-deep layer. The effectiveness of the evaluation results is verified by static and dynamic combination, and countermeasures to improve gas reservoir recovery are proposed. The results indicate that: (1) The non-uniform water invasion of fractures is jointly controlled by structural position, fracture development degree, and fracture network combination, which can be divided into three modes: edge water channeling along the large fracture in the core, edge and bottom water invading along the fracture in the wing, and rapid, violent water flooding of the bottom water along the fracture/small fault in the low part. (2) The replacement coefficient of water invasion in the three typical blocks is 0.2–0.3, indicating that they are sub active water-gas reservoirs. However, the severity of water-sealed gas varies greatly. The more severe the water-sealed gas is, the lower the recovery factor of the gas reservoir. (3) For directionally penetrating large fracture gas reservoirs, water shutoff should be carried out. For fracture network gas reservoirs with high fracture density, mild exploitation can control water, and early drainage can reduce the impact of water invasion, improving gas reservoir recovery. It is concluded that the new method of water-sealed gas dynamic evaluation can provide a reliable basis for evaluating fracture non-uniform water invasion dynamics of the ultra-deep gas reservoir and enhancing oil recovery of the gas reservoir in the Kuqa Depression. This method also supports the formulation of water control policies and the economic and efficient development of ultra-deep gas reservoirs in the Kuqa Depression

A High-Voltage Low-Standby-Power Startup Circuit Using Monolithically Integrated E/D-Mode AlGaN/GaN MIS-HEMTs

We experimentally demonstrate a high-voltage low-standby power startup circuit for powering up the off-line switched-mode power supply (SMPS) during the startup period by exploiting monolithically integrated enhancement/depletion-mode metal-insulator-semiconductor high electron mobility transistors (E/D-mode MIS-HEMTs) fabricated on a GaN-on-Si power device platform. The E/D-mode MIS-HEMTs exhibit a threshold voltage of +1.2 and -11 V, respectively. The high-voltage D-mode device used in the demonstration features an OFF-state breakdown voltage of 640 V and a safe operating area with a thermal limitation of 11.6 W/mm, whereas the low-voltage E-mode device features a source-gate breakdown voltage of 98 V, satisfying the requirement of the startup circuit. The functionality of the startup circuit is successfully achieved with an input voltage range 10-200 V and a startup current of 1.08 mA