Erasure List-Decodable Codes from Random and Algebraic Geometry Codes

Erasure list decoding was introduced to correct a larger number of erasures with output of a list of possible candidates. In the present paper, we consider both random linear codes and algebraic geometry codes for list decoding erasure errors. The contributions of this paper are two-fold. Firstly, we show that, for arbitrary $00$ ($R$ and $\epsilon$ are independent), with high probability a random linear code is an erasure list decodable code with constant list size $2^{O(1/\epsilon)}$ that can correct a fraction $1-R-\epsilon$ of erasures, i.e., a random linear code achieves the information-theoretic optimal trade-off between information rate and fraction of erasure errors. Secondly, we show that algebraic geometry codes are good erasure list-decodable codes. Precisely speaking, for any $0<R<1$ and $\epsilon>0$, a $q$-ary algebraic geometry code of rate $R$ from the Garcia-Stichtenoth tower can correct $1-R-\frac{1}{\sqrt{q}-1}+\frac{1}{q}-\epsilon$ fraction of erasure errors with list size $O(1/\epsilon)$. This improves the Johnson bound applied to algebraic geometry codes. Furthermore, list decoding of these algebraic geometry codes can be implemented in polynomial time

EpCAM Is an Endoderm-Specific Wnt Derepressor that Licenses Hepatic Development

SummaryMechanisms underlying cell-type-specific response to morphogens or signaling molecules during embryonic development are poorly understood. To learn how response to the liver-inductive Wnt2bb signal is achieved, we identify an endoderm-enriched, single transmembrane protein, epithelial-cell-adhesion-molecule (EpCAM), as an endoderm-specific Wnt derepressor in zebrafish. hi2151/epcam mutants exhibit defective liver development similar to prt/wnt2bb mutants. EpCAM directly binds to Kremen1 and disrupts the Kremen1-Dickkopf2 (Dkk2) interaction, which prevents Kremen1-Dkk2-mediated removal of Lipoprotein-receptor-related protein 6 (Lrp6) from the cell surface. These data lead to a model in which EpCAM derepresses Lrp6 and cooperates with Wnt ligand to activate Wnt signaling through stabilizing membrane Lrp6 and allowing Lrp6 clustering into active signalosomes. Thus, EpCAM cell autonomously licenses and cooperatively activates Wnt2bb signaling in endodermal cells. Our results identify EpCAM as the key molecule and its functional mechanism to confer endodermal cells the competence to respond to the liver-inductive Wnt2bb signal

Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search

Online relevance matching is an essential task of e-commerce product search to boost the utility of search engines and ensure a smooth user experience. Previous work adopts either classical relevance matching models or Transformer-style models to address it. However, they ignore the inherent bipartite graph structures that are ubiquitous in e-commerce product search logs and are too inefficient to deploy online. In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models. Especially for the core student model of the framework, we propose a novel method using $k$-order relevance modeling. The experimental results on large-scale real-world data (the size is 6$\sim$174 million) show that the proposed method significantly improves the prediction accuracy in terms of human relevance judgment. We deploy our method to the anonymous online search platform. The A/B testing results show that our method significantly improves 5.7% of UV-value under price sort mode

Highly Sensitive Electrochemical Sensor for the Determination of 8-Hydroxy-2 \u27-deoxyguanosine Incorporating SWCNTs-Nafion Composite Film

8-Hydroxy-2\u27-deoxyguanosine (8-OHdG) is a typical biomarker of oxidative DNA damage and has attracted much attention in recent years since the level of 8-OHdG in body fluids is typically associated with various diseases. In this work, a simple and highly sensitive electrochemical sensor for the determination of 8-OHdG was fabricated incorporating single wall carbon nanotubes-(SWCNTs-) Nafion composite film coated on glassy carbon electrode. Nafion was chosen as an optimal adhesive agent from a series of adhesive agents and acted as a binder, enrichment, and exclusion film. Due to the strong cation-exchange ability of Nafion and the outstanding electronic properties ofSWCNTs, the prepared SWCNTs-Nafion film can strongly enhance the electrochemical response to oxidation of 8-OHdG and efficiently alleviate the interferences from uric acid and ascorbic acid. The oxidation peak currents are linear with the concentration of 8-OHdG in the range of 0.03 to 1.25 mu M with a detection limit of 8.0 nM (S/N = 3). This work demonstrates that SWCNTs-Nafion film can improve the sensitivity, selectivity, reproducibility, and stability, making it an ideal candidate for electrochemical detection of 8-OHdG

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible.Comment: Accepted by AAAI2

An achiral magnetic photonic antenna as a tunable nanosource of superchiral light

Sensitivity to molecular chirality is crucial for many fields, from biology and chemistry to the pharmaceutical industry. By generating superchiral light, nanophotonics has brought innovative solutions to reduce the detection volume and increase sensitivity at the cost of a non-selectivity of light chirality or a strong contribution to the background. Here, we theoretically propose an achiral plasmonic resonator, based on a rectangular nanoslit in a thin gold layer behaving as a magnetic dipole, to generate a tunable nanosource of purely superchiral light. This nanosource is free of any background, and the sign of its chirality is externally tunable in wavelength and polarization. These properties result from the coupling between the incident wave and the magnetic dipolar character of our nano-antenna. Thus, our results propose a platform with deep subwavelength detection volumes for chiral molecules in particular, in the visible, and a roadmap for optimizing the signal-to-noise ratios in circular dichroism measurements to reach single-molecule sensitivity

Controlled manipulation of oxygen vacancies using nanoscale flexoelectricity

Oxygen vacancies, especially their distribution, are directly coupled to the electromagnetic properties of oxides and related emergent functionalities that have implication in device applications. Here using a homoepitaxial strontium titanate thin film, we demonstrate a controlled manipulation of the oxygen vacancy distribution using the mechanical force from a scanning probe microscope tip. By combining Kelvin probe force microscopy imaging and phase-field simulations, we show that oxygen vacancies can move under a stress-gradient-induced depolarisation field. When tailored, this nanoscale flexoelectric effect enables a controlled spatial modulation. In motion, the scanning probe tip thereby deterministically reconfigures the spatial distribution of vacancies. The ability to locally manipulate oxygen vacancies on-demand provides a tool for the exploration of mesoscale quantum phenomena, and engineering multifunctional oxide devices.Comment: 35 pages, Main text and the supplementary information combine

Oxygen Partial Pressure during Pulsed Laser Deposition: Deterministic Role on Thermodynamic Stability of Atomic Termination Sequence at SrRuO3/BaTiO3 Interface

With recent trends on miniaturizing oxide-based devices, the need for atomic-scale control of surface/interface structures by pulsed laser deposition (PLD) has increased. In particular, realizing uniform atomic termination at the surface/interface is highly desirable. However, a lack of understanding on the surface formation mechanism in PLD has limited a deliberate control of surface/interface atomic stacking sequences. Here, taking the prototypical SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) heterostructure as a model system, we investigated the formation of different interfacial termination sequences (BaO-RuO2 or TiO2-SrO) with oxygen partial pressure (PO2) during PLD. We found that a uniform SrO-TiO2 termination sequence at the SRO/BTO interface can be achieved by lowering the PO2 to 5 mTorr, regardless of the total background gas pressure (Ptotal), growth mode, or growth rate. Our results indicate that the thermodynamic stability of the BTO surface at the low-energy kinetics stage of PLD can play an important role in surface/interface termination formation. This work paves the way for realizing termination engineering in functional oxide heterostructures.Comment: 27 pages, 6 figures, Supporting Informatio

Characteristics of DNA-AuNP networks on cell membranes and real-time movies for viral infection

AbstractThis data article provides complementary data for the article entitled “DNA-AuNP networks on cell membranes as a protective barrier to inhibit viral attachment, entry and budding” Li et al. (2016) [1]. The experimental methods for the preparation and characterization of DNA-conjugated nanoparticle networks on cell membranes were described. Confocal fluorescence images, agarose gel electrophoresis images and hydrodynamic diameter of DNA-conjugated gold nanoparticle (DNA-AuNP) networks were presented. In addition, we have prepared QDs-labeled RSV (QDs-RSV) to real-time monitor the RSV infection on HEp-2 cells in the absence and presence of DNA-AuNP networks. Finally, the cell viability of HEp-2 cells coated by six types of DNA-nanoparticle networks was determined after RSV infection