Recent years have witnessed fast developments of graph neural networks (GNNs) that benefited myriads analytic tasks and applications. In general, most GNNs depend on the homophily assumption nodes belonging to same class are more likely be connected. However, as a ubiquitous property in numerous real-world scenarios, heterophily, i.e., with different labels tend linked, significantly limits performance tailor-made homophilic GNNs. Hence, for heterophilic graphs gaining increasing research...

One-Shot Neural Architecture Search (NAS) significantly improves the computational efficiency through weight sharing. However, this approach also introduces multi-model forgetting during supernet training (architecture search phase), where performance of previous architectures degrade when sequentially new with partially-shared weights. To overcome such catastrophic forgetting, state-of-the-art method assumes that shared weights are optimal jointly optimizing a posterior probability. strict...

Abstract The squeezed cat state, an essential quantum resource, can be used for error correction and slowing decoherence of the optical state. However, preparing a state with high generation rate, effectively manipulating it, remain challenging. In this work, high‐performance all‐optical in‐line squeezer is developed to prepare manipulate phase quadrature squeezing. This scheme has advantages that squeezing manipulated by changing working condition squeezer, higher rate achieved via...

Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as search proceeds. Rather than directly optimizing architecture parameters, this paper formulates neural a distribution learning problem relaxing...

In recent years, Graph Neural Network (GNN) approaches with enhanced knowledge graphs (KG) perform well in question answering (QA) tasks. One critical challenge is how to effectively utilize interactions between the QA context and KG. However, existing work only adopts identical representation interact multiple layers of KG, which results a restricted interaction. this paper, we propose DRLK (Dynamic Hierarchical Reasoning Language Model Knowledge Graphs), novel model that utilizes dynamic...

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant uncertainty when inferring on unseen unlabeled test graphs, due to mismatched training-test distributions. In this paper, we study a new problem, evaluation, that aims assess specific trained labeled observed by precisely estimating its (e.g., node classification accuracy) graphs without labels. Concretely, propose two-stage...

A simple and sensitive method for the analysis of three chlorophenols (CPs) in toilet paper has been developed first time. Acetone was used as a solvent to extract CPs from assisted by ultrasound irradiation. When extraction process finished, 0.5 mL acetone subjected synchronous derivative dispersive liquid–liquid microextraction (DLLME). During this process, analytes were rapidly transferred another (chlorobenzene) further clean-up enrichment. could also act dispersant during DLLME which...

The weather conditions affect the approach and departure routings of aircraft. detection flight status aircraft faces two major issues under adverse conditions. They are how to select an approximate effective feature combination from different parameters identify via a reasonable parameter combination. This article presents solution recognition problem time-domain wavelet-domain features extracted complete dataset typical dataset, respectively. ARMA coefficients entropy is also represent...

Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The theoretical research date on has focused primarily expressive power rather than trainability, an optimization perspective. Compared expressivity, trainability attempts address a fundamental...

Abstract Diagnosis prediction exploits electronic health records (EHRs) to predict the future diagnoses of patients, further supporting clinical decision making and personalized treatments. However, a patient's EHR is an irregular sequence visits that contains large number medical concepts. The disease progression patterns are closely related visits, as well contextual knowledge each visit. existing diagnosis methods ignore complex relationships between knowledge, thus cannot achieve...

Chinese Spell Checking (CSC) is a widely used technology, which plays vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by scale foundation model, BERT-based method does not work well few-shot scenarios, showing certain limitations practical applications. In this paper, we explore using an in-context learning named RS-LLM (Rich Semantic based LLMs)...

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students' proficiency on knowledge concepts from their response logs. Current works assume each concept will certainly be tested covered by multiple exercises. However, whether online or offline courses, it's hardly feasible completely cover all several Restricted tests lead undiscovered deficits, especially untested concepts(UKCs). In this paper, we propose novel \underline{Dis}entangling...

Multi-modal entity alignment aims to integrate equivalent entities in diverse multi-modal knowledge graphs. However, previous studies have primarily focused on fusion methods without considering the impact of graph structure heterogeneity tasks due distinct structural features different Moreover, information that enriches representations, including image, textual content, and numerical attributes, frequently incorporates noise modal discrepancies. To tackle these challenges, we introduce...

A novel poly(2-hydroxypropyl methacrylate-ethylene dimethacrylate) (HPMA-EDMA) monolithic capillary column was synthesized and selected as the extraction medium for polymer monolith microextraction (PMME).

An exact controlled-NOT gate with a single trapped cold ion is usually implemented by sequentially using three laser pulses. Here, we show that two-step driving sufficient to realize such gate, as long the initial phases and durations of applied pulses relevant Lamb?Dicke parameter are properly set.