Generalized Ces\`{a}ro-like operator from a class of analytic function spaces to analytic Besov spaces

Let $\mu$ be a finite positive Borel measure on $[0,1)$ and $f(z)=\sum_{n=0}^{\infty}a_{n}z^{n} \in H(\mathbb{D})$. For $0<\alpha<\infty$, the generalized Ces\`aro-like operator $\mathcal{C}_{\mu,\alpha}$ is defined by $$\mathcal {C}_{\mu,\alpha}(f)(z)=\sum^\infty_{n=0}\left(\mu_n\sum^n_{k=0}\frac{\Gamma(n-k+\alpha)}{\Gamma(\alpha)(n-k)!}a_k\right)z^n, \ z\in \mathbb{D},$$ where, for $n\geq 0$, $\mu_n$ denotes the $n$-th moment of the measure $\mu$, that is, $\mu_n=\int_{0}^{1} t^{n}d\mu(t)$. For $s>1$, let $X$ be a Banach subspace of $H(\mathbb{D})$ with $\Lambda^{s}_{\frac{1}{s}}\subset X\subset\mathcal {B}$. In this paper, for $1\leq p <\infty$, we characterize the measure $\mu$ for which $\mathcal{C}_{\mu,\alpha}$ is bounded(or compact) from $X$ into analytic Besov space $B_{p}$

Generalized integral type Hilbert operator acting on weighted Bloch space

Let $\mu$ be a finite Borel measure on $[0,1)$. In this paper, we consider the generalized integral type Hilbert operator $$\mathcal{I}_{\mu_{\alpha+1}}(f)(z)=\int_{0}^{1}\frac{f(t)}{(1-tz)^{\alpha+1}}d\mu(t)\ \ \ (\alpha>-1).$$ The operator $\mathcal{I}_{\mu_{1}}$ has been extensively studied recently. The aim of this paper is to study the boundedness(resp. compactness) of $\mathcal{I}_{\mu_{\alpha+1}}$ acting from the normal weight Bloch space into another of the same kind. As consequences of our study, we get completely results for the boundedness of $\mathcal{I}_{\mu_{\alpha+1}}$ acting between Bloch type spaces, logarithmic Bloch spaces among others.Comment: arXiv admin note: text overlap with arXiv:2208.1074

HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

Blockholder Mutual Fund Participation in Private In-House Meetings

The Shenzhen Stock Exchange (SZSE) in China is unique worldwide in requiring disclosure of the timing, participants, and selected content of private in-house meetings between firm managers and outsider investors. We investigate whether these private meetings benefit hosting firms and their major outside institutional investors—blockholder mutual funds (i.e., funds with ownership ≥5%). Using a large data set of SZSE firms, we find that blockholder mutual funds have more access to private in-house meetings, and top management is more likely to be present, especially when a meeting is associated with negative news. Furthermore, when blockholder mutual funds attend negative-news meetings with top management, they are less likely to sell shares, their investment relationship with the hosting firm lasts longer, and hosting firms experience lower postmeeting stock return volatility. These findings suggest that private in-house meetings are an informative disclosure channel that improves social bonding between top management and blockholder mutual funds in ways that benefit hosting firms

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs.

BackgroundPolycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic subunit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are characterized by high tissue-specificity; however, little is known about the tissue profile of the EZH2- interacting lncRNAs.ObjectiveHere we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno- precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In addition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, majority of the lncRNAs were firstly reported to be associated with EZH2.ConclusionOur findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Several Integral Estimates and Some Applications

In this paper, the authors first consider the bidirectional estimates of several typical integrals. As some applications of these integral estimates, the authors investigate the pointwise multipliers from the normal weight general function space $F(p,\mu,s)$ to the normal weight Bloch type space $\mathcal{B_{\nu}}(B_{n})$ on the unit ball $B_{n}$ of $\mathbb{C}^{n}$, where $\mu$ and $\nu$ are two normal functions on $[0,1)$. For the special normal function $\displaystyle{\mu(r)=(1-r^{2})^{\alpha}\log^{\beta}\frac{e}{1-r^{2}}}$ ($\alpha>0$, $-\infty<\beta<\infty$), the authors give the necessary and sufficient conditions of pointwise multipliers from $F(p,\mu,s)$ to $\mathcal{B_{\nu}}(B_{n})$ for all cases