Distilling Word Embeddings: An Encoding Approach

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.Comment: Accepted by CIKM-16 as a short paper, and by the Representation Learning for Natural Language Processing (RL4NLP) Workshop @ACL-16 for presentatio

Tin-graphene tubes as anodes for lithium-ion batteries with high volumetric and gravimetric energy densities.

Limited by the size of microelectronics, as well as the space of electrical vehicles, there are tremendous demands for lithium-ion batteries with high volumetric energy densities. Current lithium-ion batteries, however, adopt graphite-based anodes with low tap density and gravimetric capacity, resulting in poor volumetric performance metric. Here, by encapsulating nanoparticles of metallic tin in mechanically robust graphene tubes, we show tin anodes with high volumetric and gravimetric capacities, high rate performance, and long cycling life. Pairing with a commercial cathode material LiNi0.6Mn0.2Co0.2O2, full cells exhibit a gravimetric and volumetric energy density of 590 W h Kg-1 and 1,252 W h L-1, respectively, the latter of which doubles that of the cell based on graphite anodes. This work provides an effective route towards lithium-ion batteries with high energy density for a broad range of applications

Phase Identification with Incomplete Data

Phase identification is a process to determine which of the three phases a particular house is connected to. The state-of-the-art identification methods usually exploit smart metering data. However, the data sets are not always available and the major challenge is hence to identify phases with incomplete data set. This paper proposes a novel spectral and saliency analysis identification method to overcome this hurdle. Spectral analysis is first performed to extract the high-frequency features from the incomplete data. Saliency analysis is then adopted to extract salient features from the variations of high-frequency loads in the time domain. Correlation analysis between customer features and the phase features is used to determine customers' phase connectivity. The method is executed iteratively until all customers with smart meters have been allocated to a specific phase or no salient features can be found. It is validated against real data from over 6000 smart meters in Ireland and achieves an accuracy of over 93% with only 10% smart meter penetration ratio in a 100-household network.</p

High-quality mesoporous graphene particles as high-energy and fast-charging anodes for lithium-ion batteries.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g-1 at 0.2 C or 440 mA h g-1 at 60 C with a mass loading of 1 mg cm-2), and excellent cycling stability (e.g., &gt;99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm-2). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm-2 at 0.9 mA cm-2), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance

Milk fat globule membrane protein promotes C2C12 cell proliferation through the PI3K/Akt signaling pathway

Milk fat globule membrane (MFGM) protein is known to have several health benefits, including an anti-sarcopenia effect; however, its mechanism is unclear. The aim of this study was to investigate the potential mechanism of action of the MFGM protein. The MFGM protein was extracted and separated into 4 fractions, and Fraction 2 (57 % of total MFGM) demonstrated the greatest effect on C2C12 cell proliferation. Milk fat globule-EGF factor 8 (MFG-E8) accounted for 82.35 % of the MFGM protein. The effects of whole Fraction 2 (100 μg/mL, 200 μg/mL and 300 μg/mL) on cell proliferation and morphology were measured. Using qRT-PCR or a Western blot assay, several regulatory factors, e.g., PI3K P85α, p-pI3K p85α (Tyr 508), Akt, p-Akt (Ser 473), mTOR and p-mTOR (Ser 2448), were measured in cells incubated with 200 μg/mL of Fraction 2 with or without wortmannin. The results demonstrated that Fraction 2 induced C2C12 cell proliferation in a dose-dependent manner, upregulated the mRNA expression of mTOR and p70S6K, and activated PI3K, Akt, mTOR and P70S6K phosphorylation; however, Fraction 2 inhibited FOXO3a and 4E-BP. The results demonstrate that the MFGM protein, predominantly MFG-E8, promotes cell proliferation through the PI3K/Akt/mTOR signaling pathway. This study elucidated the molecular mechanism of the MFGM protein, primarily MFG-E8, in promoting C2C12 cell proliferation via the PI3K/Akt/mTOR/P70S6K signal pathway