Enhancing Domain Word Embedding via Latent Semantic Imputation

We present a novel method named Latent Semantic Imputation (LSI) to transfer external knowledge into semantic space for enhancing word embedding. The method integrates graph theory to extract the latent manifold structure of the entities in the affinity space and leverages non-negative least squares with standard simplex constraints and power iteration method to derive spectral embeddings. It provides an effective and efficient approach to combining entity representations defined in different Euclidean spaces. Specifically, our approach generates and imputes reliable embedding vectors for low-frequency words in the semantic space and benefits downstream language tasks that depend on word embedding. We conduct comprehensive experiments on a carefully designed classification problem and language modeling and demonstrate the superiority of the enhanced embedding via LSI over several well-known benchmark embeddings. We also confirm the consistency of the results under different parameter settings of our method.Comment: ACM SIGKDD 201

Modeling Relation Paths for Representation Learning of Knowledge Bases

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.Comment: 10 page

MaxCut in graphs with sparse neighborhoods

Let $G$ be a graph with $m$ edges and let $\mathrm{mc}(G)$ denote the size of a largest cut of $G$. The difference $\mathrm{mc}(G)-m/2$ is called the surplus $\mathrm{sp}(G)$ of $G$. A fundamental problem in MaxCut is to determine $\mathrm{sp}(G)$ for $G$ without specific structure, and the degree sequence $d_1,\ldots,d_n$ of $G$ plays a key role in getting the lower bound of $\mathrm{sp}(G)$. A classical example, given by Shearer, is that $\mathrm{sp}(G)=\Omega(\sum_{i=1}^n\sqrt d_i)$ for triangle-free graphs $G$, implying that $\mathrm{sp}(G)=\Omega(m^{3/4})$. It was extended to graphs with sparse neighborhoods by Alon, Krivelevich and Sudakov. In this paper, we establish a novel and stronger result for a more general family of graphs with sparse neighborhoods. Our result can derive many well-known bounds on $\mathrm{sp}(G)$ in $H$-free graphs $G$ for different $H$, such as the triangle, the even cycle, the graphs having a vertex whose removal makes the graph acyclic, or the complete bipartite graph $K_{s,t}$ with $s\in \{2,3\}$. It can also deduce many new (tight) bounds on $\mathrm{sp}(G)$ in $H$-free graphs $G$ when $H$ is any graph having a vertex whose removal results in a bipartite graph with relatively small Tur\'{a}n number, especially the even wheel. This contributes to a conjecture raised by Alon, Krivelevich and Sudakov. Moreover, we give a new family of graphs $H$ such that $\mathrm{sp}(G)=\Omega(m^{3/4+\epsilon(H)})$ for some constant $\epsilon(H)>0$ in $H$-free graphs $G$, giving an evidence to a conjecture suggested by Alon, Bollob\'as, Krivelevich and Sudakov

Polarizing intestinal epithelial cells electrically through Ror2

© 2014. Published by The Company of Biologists Ltd.Peer reviewedPublisher PD

Ni-Doped Sr\u3csub\u3e2\u3c/sub\u3eFe\u3csub\u3e1.5\u3c/sub\u3eMo\u3csub\u3e0.5\u3c/sub\u3eO\u3csub\u3e6-δ\u3c/sub\u3e as Anode Materials for Solid Oxide Fuel Cells

10% Ni-doped Sr2Fe1.5Mo0.5O6-δ with A-site deficiency is prepared to induce in situ precipitation of B-site metals under anode conditions in solid oxide fuel cells. XRD, SEM and TEM results show that a significant amount of nano-sized Ni-Fe alloy metal phase has precipitated out from Sr1.9Fe1.4Ni0.1Mo0.5O6-δ upon reduction at 800◦C in H2. The conductivity of the reduced composite reaches 29 S cm−1 at 800◦C in H2. Furthermore, fuel cell performance of the composite anode Sr1.9Fe1.4Ni0.1Mo0.5O6-δ-SDC is investigated using H2 as fuel and ambient air as oxidant with La0.8Sr0.2Ga0.87Mg0.13O3 electrolyte and La0.6Sr0.4Co0.2Fe0.8O3 cathode. The cell peak power density reaches 968 mW cm−2 at 800◦C and the voltage is relatively stable under a constant current load of 0.54 A cm−2. After 5 redox cycles of the anode at 800◦C, the fuel cell performance doesn’t suffer any degradation, indicating good redox stability of Sr1.9Fe1.4Ni0.1Mo0.5O6-δ. Peak power density of 227 mW cm−2 was also obtained when propane is used as fuel. These results indicate that a self-generated metal-ceramic composite can been successfully derived from Sr2Fe1.5Mo0.5O6-δ by compositional modifications and Sr1.9Fe1.4Ni0.1Mo0.5O6-δ is a very promising solid oxide fuel cell anode material with enhanced catalytic activity and inherited good redox stability from the parent ceramic material

MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric