Local linear m-estimators in null recurrent time series

In this paper, we study a nonlinear cointegration type model Yκ = m(Xκ) + wκ, where {Yκ} and {Xκ} are observed nonstationary processes and {Wκ} is an unobserved stationary process. The process {Xκ} is assumed to be a null-recurrent Markov chain. We apply a robust version of local linear regression smoothers to estimate m(-). Under mild conditions, the uniform weak consistency and asymptotic normality of the local linear M-estimators are established. Furthermore, a one-step iterated procedure is introduced to obtain the local linear M-estimator and the optimal bandwidth selection is discussed. Meanwhile, some numerical examples are given to show that the proposed theory and methods perform well in practice

A sex-specific association of common variants of neuroligin genes (NLGN3 and NLGN4X) with autism spectrum disorders in a Chinese Han cohort

<p>Abstract</p> <p>Background</p> <p>Synaptic genes, <it>NLGN3 </it>and <it>NLGN4X</it>, two homologous members of the neuroligin family, have been supposed as predisposition loci for autism spectrum disorders (ASDs), and defects of these two genes have been identified in a small fraction of individuals with ASDs. But no such rare variant in these two genes has as yet been adequately replicated in Chinese population and no common variant has been further investigated to be associated with ASDs.</p> <p>Methods</p> <p>7 known ASDs-related rare variants in <it>NLGN3 </it>and <it>NLGN4X </it>genes were screened for replication of the initial findings and 12 intronic tagging single nucleotide polymorphisms (SNPs) were genotyped for case-control association analysis in a total of 229 ASDs cases and 184 control individuals in a Chinese Han cohort, using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry.</p> <p>Results</p> <p>We found that a common intronic variant, SNP rs4844285 in <it>NLGN3 </it>gene, and a specific 3-marker haplotype X^A-X^G-X^T(rs11795613-rs4844285-rs4844286) containing this individual SNP were associated with ASDs and showed a male bias, even after correction for multiple testing (SNP allele: P = 0.048, haplotype:P = 0.032). Simultaneously, none of these 7 known rare mutation of <it>NLGN3</it> and <it>NLGN4X</it> genes was identified, neither in our patients with ASDs nor controls, giving further evidence that these known rare variants might be not enriched in Chinese Han cohort.</p> <p>Conclusion</p> <p>The present study provides initial evidence that a common variant in <it>NLGN3 </it>gene may play a role in the etiology of ASDs among affected males in Chinese Han population, and further supports the hypothesis that defect of synapse might involvement in the pathophysiology of ASDs.</p

PIE: Physics-inspired Low-light Enhancement

In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods often training a LLE model with strict pixel-correspondence image pairs, we eliminate the need for pixel-correspondence paired training data and instead train with unpaired images. (ii) To address the disregard for negative samples and the inadequacy of their generation in existing methods, we incorporate physics-inspired contrastive learning for LLE and design the Bag of Curves (BoC) method to generate more reasonable negative samples that closely adhere to the underlying physical imaging principle. (iii) To overcome the reliance on semantic ground truths in existing methods, we propose an unsupervised regional segmentation module, ensuring regional brightness consistency while eliminating the dependency on semantic ground truths. Overall, the proposed PIE can effectively learn from unpaired positive/negative samples and smoothly realize non-semantic regional enhancement, which is clearly different from existing LLE efforts. Besides the novel architecture of PIE, we explore the gain of PIE on downstream tasks such as semantic segmentation and face detection. Training on readily available open data and extensive experiments demonstrate that our method surpasses the state-of-the-art LLE models over six independent cross-scenes datasets. PIE runs fast with reasonable GFLOPs in test time, making it easy to use on mobile devices.Comment: arXiv admin note: text overlap with arXiv:2112.0645

Multiband superconductivity and a deep gap minimum evidenced by specific heat in KCa2_2(Fe1−x_{1-x}Nix_x)4_4As4_4F2_2

Specific heat can explore low-energy quasiparticle excitations of superconductors, so it is a powerful tool for bulk measurement on the superconducting gap structure and pairing symmetry. Here, we report an in-depth investigation on the specific heat of the multiband superconductors KCa$_2$(Fe$_{1-x}$Ni$_x$)$_4$As$_4$F$_2$ ($x$ = 0, 0.05, 0.13) single crystals and the overdoped non-superconducting one with $x$ = 0.17. For the samples with $x$ = 0 and $x$ = 0.05, the magnetic field induced specific heat coefficient $\Delta\gamma(H)$ in the low temperature limit increases rapidly below 2 T, then it rises slowly above 2 T. Using the non-superconducting sample with $x$ = 0.17 as a reference, and applying a mixed model that combines Debye and Einstein modes, the specific heat of phonon background for various superconducting samples can be fitted and the detailed information of the electronic specific heat is obtained. Through comparative analyses, it is found that the energy gap structure including two $s$-wave gaps and an extended $s$-wave gap with large anisotropy can reasonably describe the electronic specific heat data. According to these results, we suggest that at least one anisotropic superconducting gap with a deep gap minimum should exist in this multiband system. With the doping of Ni, the $T_c$ of the sample decreases along with the decrease of the large $s$-wave gap, but the extended $s$-wave gap increases due to the enlarged electron pockets via adding more electrons. Despite these changes, the general properties of the gap structure remain unchanged versus doping Ni. In addition, the calculation of condensation energy of the parent and doped samples shows the rough consistency with the correlation of $U_0 \propto {T_c}^n$ with $n$ = 3-4, which is beyond the understanding of the BCS theory

Effective Few-Shot Named Entity Linking by Meta-Learning

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability.Comment: 14 pages, 4 figures. Accepted at IEEE ICDE 202

Differential display identifies overexpression of the USP36 gene, encoding a deubiquitinating enzyme, in ovarian cancer

Objectives. To find potential diagnostic markers or therapeutic targets, we used differential display technique to identify genes that are over or under expressed in human ovarian cancer. Methods. Genes were initially identified by differential display between two human ovarian surface epithelium cultures and two ovarian cancer cell lines, A2780 and Caov-3. Genes were validated by relative quantitative RT-PCR and RNA in situ hybridization. Results. Twenty-eight non-redundant sequences were expressed differentially in the normal ovarian epithelium and ovarian cancer cell lines. Seven of the 28 sequences showed differential expression between normal ovary and ovarian cancer tissue by RT-PCR. USP36 was over-expressed in ovarian cancer cell lines and tissues by RT-PCR and RNA in situ hybridization. Northern blot analysis and RT-PCR revealed two transcripts for USP36 in ovarian tissue. The major transcript was more specific for ovarian cancer and was detected by RT-PCR in 9/9 ovarian cancer tissues, 3/3 cancerous ascites, 5/14 (36%) sera from patients with ovarian cancer, and 0/7 sera from women without ovarian cancer. Conclusion. USP36 is overexpressed in ovarian cancer compared to normal ovary and its transcripts were identified in ascites and serum of ovarian cancer patients