Modern mainstream programming languages, such as TypeScript, Flow, and Scala, have polymorphic type systems enriched with intersection union types. These languages implement variants of bidirectional higher-rank inference, which was previously studied mostly in the context functional programming. However, existing inference implementations lack solid theoretical foundations when dealing non-structural subtyping types, were not before. In this paper, we study explicit applications, types...

To promote the real-time dispatching of a power grid and balanced decision-making producers, accuracy forecasting are two main problems that need to be solved in ultra-short-term photovoltaic (PV) forecasting. Focusing on slow model training speed low due redundancy data samples insufficient long periodic capture complex weather, this paper proposes two-stage method for PV based data-driven. In meteorological analysis stage, generation similar forecast day were extracted by inputting daily...

Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large-scale private data collected from user side, GNN may not be able to reflect the excellent performance, without rich features and complete adjacent relationships. Addressing problem, vertical federated learning (VFL) is proposed implement local protection through training a global model collaboratively. Consequently, for graph-structured data, it natural idea construct GNN-based VFL...

The task of cervical cell classification can be divided into four sub-tasks: (1) the isolation single cells, clusters and clumps as well artifacts, (2) segmentation image nucleus cytoplasm, (3) extraction features such size density grey level extrema, fractal dimension, texture parameters shape measures, (4) use these to classify normal or abnormal. final problem formulating a diagnostic decision based on data is multivariate statistical one, which there are many theoretical practical...

When the extension state of non-ellipsoidal extended target (NET) changes, performance traditional multiple tracking algorithms based on constant number sub-objects will decrease. To solve this problem, paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for targets with varying sub-objects, called VN-NET-GGIW-PHD filter. In proposed filter, each NET is considered as combination spatially close and label management introduced to realize association between...

Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks many real-world applications. The competition between attack and defense methods also enhances the robustness of GNNs. In this competition, development adversarial training put forward higher requirement for diversity examples. By contrast, most with specific strategies are difficult to satisfy such a requirement. To address problem, we propose GraphAttacker, novel generic framework that can flexibly adjust...

Mainstream object-oriented programming languages such as Java, Scala, C#, or TypeScript have polymorphic type systems with subtyping and bounded quantification. Bounded quantification, despite being a pervasive widely used feature, has attracted little research work on type-inference algorithms to support it. A notable exception is local inference, which the basis of most current implementations inference for mainstream languages. However, quantification in important restrictions, its...

Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large scale private data collected from user-side, GNN may not be able to reflect the excellent performance, without rich features and complete adjacent relationships. Addressing problem, vertical federated learning (VFL) is proposed implement local protection through training a global model collaboratively. Consequently, for graph-structured data, it natural idea construct based VFL...

Abstract The use of an initial state value function and optimal strategy are used in this paper to solve educational problems based on deep reinforcement learning. Deep learning’s approximate is defined, the matrix model created by training tuning using learning methods like gradient descent. To analyze modeling process learning, reward values added Markov decision transfer expected cumulative returns calculated. weights trained Bellman equation enhance algorithm’s stability. In evaluating...

With the wide application of large-capacity and high-speed optical networks, its security has become a current research focus. Due to limitations traditional key distribution encryption schemes, this paper proposes an innovative control scheme based on physical layer channel feature extraction analysis. First all, transceiver terminal extracts characteristics fiber separately, generates consensus through quantization coding. Second, use generated base encrypt transmission sequence, map...

The aim of the present study was to investigate differential modules (DMs) between uveal melanoma (UM) and normal conditions by examining networks. Based on a gene expression profile collected from ArrayExpress database, inference DMs involved three steps: first step construction co‑expression network (DCN); second, module algorithm adapted identify presented in DCN; finally, statistical significance were assessed based null score distribution generated using randomized A DCN with 309 nodes...

Despite achieving success in many domains, deep learning models remain mostly black boxes. However, understanding the reasons behind predictions is quite important assessing trust, which fundamental EEG analysis task. In this work, we propose to use two representative explanation approaches, including LIME and Grad-CAM, explain of a simple convolutional neural network on an EEG-based emotional brain-computer interface. Our results demonstrate interpretability approaches provide features...

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, SMC-PHD suffers severe performance degradation. this paper, robust (RSMC-PHD) proposed. proposed filter, Student-t distribution introduced to describe noise where degrees of freedom (DOF) and scale matrix are respectively modeled as Gamma an inverse Wishart distribution. Furthermore, variational...

For an analytic self-map of the unit disk in complex plane and a nonnegative integer , composition operator followed by differentiation on is defined byfor . In this paper, we study boundedness compactness from or spaces to Bloch-type little spaces.

<p> Machine learning research has been an upcoming trend over the last few years. With more computational power and increasing volume of data available thanks to development Internet, machine methods could be applied real life problems produce fascinating outcomes. Furthermore, with rise deep methodologies, practitioners can work on unstructured datasets achieve human level accuracy. The present thesis focuses a structured dataset fields information, aiming apply multiple from...

<p> Machine learning research has been an upcoming trend over the last few years. With more computational power and increasing volume of data available thanks to development Internet, machine methods could be applied real life problems produce fascinating outcomes. Furthermore, with rise deep methodologies, practitioners can work on unstructured datasets achieve human level accuracy. The present thesis focuses a structured dataset fields information, aiming apply multiple from...