Class-Balanced and Reinforced Active Learning on Graphs

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance. To tackle this issue, we propose a novel class-balanced and reinforced active learning framework for GNNs, namely, GCBR. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. The reinforcement learning algorithm Advantage Actor-Critic (A2C) is employed to learn an optimal policy stably and efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines

Turing instability and pattern formation of a fractional Hopfield reaction–diffusion neural network with transmission delay

It is well known that integer-order neural networks with diffusion have rich spatial and temporal dynamical behaviors, including Turing pattern and Hopf bifurcation. Recently, some studies indicate that fractional calculus can depict the memory and hereditary attributes of neural networks more accurately. In this paper, we mainly investigate the Turing pattern in a delayed reaction–diffusion neural network with Caputo-type fractional derivative. In particular, we find that this fractional neural network can form steadily spatial patterns even if its first-derivative counterpart cannot develop any steady pattern, which implies that temporal fractional derivative contributes to pattern formation. Numerical simulations show that both fractional derivative and time delay have influence on the shape of Turing patterns

Effectiveness of 10 polymorphic microsatellite markers for parentage and pedigree analysis in plateau pika (Ochotona curzoniae)

<p>Abstract</p> <p>Background</p> <p>The plateau pika <it>(Ochotona curzoniae) </it>is an underground-dwelling mammal, native to the Tibetan plateau of China. A set of 10 polymorphic microsatellite loci has been developed earlier. Its reliability for parentage assignment has been tested in a plateau pika population. Two family groups with a known pedigree were used to validate the power of this set of markers.</p> <p>Results</p> <p>The error in parentage assignment using a combination of these 10 loci was very low as indicated by their power of discrimination (0.803 - 0.932), power of exclusion (0.351 - 0.887), and an effectiveness of the combined probability of exclusion in parentage assignment of 99.999%.</p> <p>Conclusion</p> <p>All the offspring of a family could be assigned to their biological mother; and their father or relatives could also be identified. This set of markers therefore provides a powerful and efficient tool for parentage assignment and other population analyses in the plateau pika.</p

MicroRNA-542 suppressed the proliferation of human glioma cells by targeting talin-2 (TLN2)

Purpose: To investigate the effect of miR-542 in the development of human glioma. Methods: The expressions of miR-542 and TLN2 in glioma cells and normal human astrocytes were determined using qRT-PCR, while MTT and colony formation assays were used to determine cell proliferation. Western blotting was used to determine protein expression. Results: It was revealed that miR-542 was significantly downregulated in glioma cells. Overexpression of miR-542 inhibited the proliferation and clonogenicity of glioma cells via induction of apoptosis. The percentage of apoptotic U87 cells increased from 5.32 in control to 26.76 upon miR-542 overexpression. Moreover, TLN2 was identified as the functional regulatory target of miR542 in glioma. The expression of TLN2 was markedly upregulated in human glioma cells. However, overexpression of miR-542 suppressed TLN2 expression. Silencing of TLN2 mimicked the tumor-suppressive effects of miR-542 in glioma cells, but this effect was blocked by TLN2 over-expression. Conclusion: These results suggest that miR-542 exerted glioma-suppressive effect, with TLN2 as its functional regulatory target. Keywords: Glioma; Proliferation; Micro-RNA; Tumorigenesis; MiR-542; Apoptosis; Prognosis; talin-2; Oncogen

High-dimensional quantum key distribution based on mutually partially unbiased bases

We propose a practical high-dimensional quantum key distribution protocol based on mutually partially unbiased bases utilizing transverse modes of light. In contrast to conventional protocols using mutually unbiased bases, our protocol uses Laguerre-Gaussian and Hermite-Gaussian modes of the same mode order as two mutually partially unbiased bases for encoding, which leads to a scheme free from mode-dependent diffraction in long-distance channels. Since only linear and passive optical elements are needed, our experimental implementation significantly simplifies qudit generation and state measurement. Since this protocol differs from conventional protocols using mutually unbiased bases, we provide a security analysis of our protocol

RESEARCH ON QUANTIFICATION OF HAZOP DEVIATION BASED ON A DYNAMIC SIMULATION AND NEURAL NETWORK

Hazard and operability (HAZOP) analysis has become more significant as the complexity of process technology has increased. However, traditional HAZOP analysis has limitations in quantifying the deviations. This work introduces artificial neural networks (ANNs) and Aspen HYSYS to explore the feasibility of HAZOP deviation quantification. With the proposed HAZOP automatic hazard analyzer (HAZOP-AHA) method, the conventional HAZOP analysis of the target process is first carried out. Second, the HYSYS dynamic model of the relevant process is established to reflect the influence of process parameters on target parameters. Third, to solve the problem of deviation identification based on multi-attribute and a large dataset, we use the ANN to process the input data. Finally, HAZOP deviation can be quantified and predicted. The method is verified by the industrial alkylation of benzene with propene to cumene. The results show that the predicted deviation severity can be close to the actual deviation severity, and the accuracy of prediction can reach nearly 100%. Thus, the method can diminish the probability of conflagration, burst, and liquid leakage

Splitting of surface defect partition functions and integrable systems

We study Bethe/gauge correspondence at the special locus of Coulomb moduli where the integrable system exhibits the splitting of degenerate levels. For this investigation, we consider the four-dimensional pure $\mathcal{N}=2$ supersymmetric $U(N)$ gauge theory, with a half-BPS surface defect constructed with the help of an orbifold or a degenerate gauge vertex. We show that the non-perturbative Dyson-Schwinger equations imply the Schr\"odinger-type and the Baxter-type differential equations satisfied by the respective surface defect partition functions. At the special locus of Coulomb moduli the surface defect partition function splits into parts. We recover the Bethe/gauge dictionary for each summand.Comment: 34 pages, 2 figures; v2. published versio

A New Information Complexity Measure for Multi-pass Streaming with Applications

We introduce a new notion of information complexity for multi-pass streaming problems and use it to resolve several important questions in data streams. In the coin problem, one sees a stream of $n$ i.i.d. uniform bits and one would like to compute the majority with constant advantage. We show that any constant pass algorithm must use $\Omega(\log n)$ bits of memory, significantly extending an earlier $\Omega(\log n)$ bit lower bound for single-pass algorithms of Braverman-Garg-Woodruff (FOCS, 2020). This also gives the first $\Omega(\log n)$ bit lower bound for the problem of approximating a counter up to a constant factor in worst-case turnstile streams for more than one pass. In the needle problem, one either sees a stream of $n$ i.i.d. uniform samples from a domain $[t]$, or there is a randomly chosen needle $\alpha \in[t]$ for which each item independently is chosen to equal $\alpha$ with probability $p$, and is otherwise uniformly random in $[t]$. The problem of distinguishing these two cases is central to understanding the space complexity of the frequency moment estimation problem in random order streams. We show tight multi-pass space bounds for this problem for every $p < 1/\sqrt{n \log^3 n}$, resolving an open question of Lovett and Zhang (FOCS, 2023); even for $1$-pass our bounds are new. To show optimality, we improve both lower and upper bounds from existing results. Our information complexity framework significantly extends the toolkit for proving multi-pass streaming lower bounds, and we give a wide number of additional streaming applications of our lower bound techniques, including multi-pass lower bounds for $\ell_p$-norm estimation, $\ell_p$-point query and heavy hitters, and compressed sensing problems.Comment: To appear in STOC 202