Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Thermal Hall effect in insulating quantum materials

The emerging field of quantum materials involves an exciting new class of materials in which charge, spin, orbital, and lattice degrees of freedom are inter- twined, exhibiting a plethora of exotic physical properties. Quantum materials include, but are not limited to, superconductors, topological quantum matter, and systems with frustrated spins, which enable a wide range of potential applications in biomedicine, energy transport and conversion, quantum sensing, and quantum information processing.S.G. and X.C. acknowledge the support from National Science Foundation under grant No. 2144328. J.Z. acknowledges the support from National Science Foundation through the Center for Dynamics and Control of Materials: an NSF MRSEC unnder Cooperative Agreement No. DMR-1720595.Center for Dynamics and Control of Material

Crystal Growth and Thermal Properties of Quasi-One-Dimensional van der Waals Material ZrSe3

ZrSe3 with a quasi-one-dimensional (quasi-1D) crystal structure belongs to the transition metal trichalcogenides (TMTCs) family. Owing to its unique optical, electrical, and optoelectrical properties, ZrSe3 is promising for applications in field effect transistors, photodetectors, and thermoelectrics. Compared with extensive studies of the above-mentioned physical properties, the thermal properties of ZrSe3 have not been experimentally investigated. Here, we report the crystal growth and thermal and optical properties of ZrSe3. Millimeter-sized single crystalline ZrSe3 flakes were prepared using a chemical vapor transport method. These flakes could be exfoliated into microribbons by liquid-phase exfoliation. The transmission electron microscope studies suggested that the obtained microribbons were single crystals along the chain axis. ZrSe3 exhibited a specific heat of 0.311 J g−1 K−1 at 300 K, close to the calculated value of the Dulong–Petit limit. The fitting of low-temperature specific heat led to a Debye temperature of 110 K and an average sound velocity of 2122 m s−1. The thermal conductivity of a polycrystalline ZrSe3 sample exhibited a maximum value of 10.4 ± 1.9 W m−1 K−1 at 40 K. The thermal conductivity decreased above 40 K and reached a room-temperature value of 5.4 ± 1.3 W m−1 K−1. The Debye model fitting of the solid thermal conductivity agreed well with the experimental data below 200 K but showed a deviation at high temperatures, indicating that optical phonons could substantially contribute to thermal transport at high temperatures. The calculated phonon mean free path decreased with temperatures between 2 and 21 K. The mean free path at 2 K approached 3 μm, which was similar to the grain size of the polycrystalline sample. This work provides useful insights into the preparation and thermal properties of quasi-1D ZrSe3

Numerical Modelling of Blasting Dust Concentration and Particle Size Distribution during Tunnel Construction by Drilling and Blasting

In order to reduce the blasting dust concentration in the tunnel during the drilling and blasting, accelerate the tunnel excavation process, and improve the working environment for the construction workers, a three-dimensional geometric model of dust transport was established based on the gas-solid two-phase flow model using the DesginModeler software, and the discrete phase model (DPM) in the FLUENT software was used to simulate the variation of dust concentration and the distribution of dust particle size at different locations along the tunnel route within 1200 s after tunnel blasting. The results showed that the concentration of blasting dust gradually decreased over time, with the fastest decrease in the range of 2 s to 120 s, and after 900 s, the dust concentration stabilized. The overall spatial distribution of the dust concentration showed a trend of decreasing from the palm face to the tunnel entrance and from the bottom plate to the upper part. The distribution pattern of dust with different particle sizes was not the same along the length of the tunnel. The large particles settled in the area of 25 m from the palm face under the action of gravity. With the increases of distance, the mass flow rate decreased, and the dust particle size became smaller, but the proportion of small particles gradually increased, while the R-R distribution index increased. The results in this study were confirmed to be reliable by comparing the measured data to provide guidance for the dust reduction technology in tunnel blasting, so as to quickly remove the dust generated during the blasting process and improve the engineering construction efficiency

Enabling blockchain applications over named data networking

Blockchain can be used to ensure trust in a decentralized environment in which no trusted authority is available. Its original idea is to collect transactions in a block, and to chain the blocks together in such a way that attackers cannot forge the chain if the majority of the network is honest. Since its creation in 2008, blockchain technology has been used broadly in Internet to support decentralized payments, cloud computing, publishing, etc. This work focuses on public permissionless blockchain which neither guards against bad actors nor enforces access control. Named data networking (NDN) uses name-based routing and in-networking caching to support efficient content delivery, making it a promising future Internet architecture as well as a great network technology which can improve blockchain data delivery. Therefore, it is a very necessary task to enable deployment of blockchain applications over NDN. However, NDN is not immediately compatible with typical blockchain, since (permissionless) blockchain applications usually require broadcasting transactions and blocks in real time, which is not supported by the “pull” design of NDN. In this work, we propose BoNDN which enables blockchain applications over NDN. Unlike previous work, BoNDN follows the core design of NDN. We treat each type of blockchain data needed to be broadcast individually. Specifically, we rely on Interest broadcasting to support real-time broadcasting of blockchain transactions, which is small in size and can be brought by an Interest packet. In addition, we propose a subscription-push approach to support broadcasting of blockchain blocks, in which each miner performs subscription, and once a block is generated, the subscribed miner will receive the block

Submicron-sized clusters of LiFePO4-C nanoparticles for lithium-ion battery cathodes

Submicron-sized clusters of LiFePO4-C nanoparticles were synthesized using a rheological phase reaction method. X-ray diffraction (XRD), scanning electron microscope (SEM), and high resolution transmission electron microscope (HRTEM) observations show that the as-prepared LiFePO4-C composite has good crystallinity consisting of submicron-sized clusters of carbon-coated LiFePO4 nanoparticles with size of around 50 nm. Both the graphite and disordered carbon are observed in the LiFePO4-C from the Raman spectroscopy and a low intensity ratio of the D band to that of the G band (ID/IG = 0.687) is obtained, confirming the high electronic conductivity of this material. The second cycle discharge capacities are 169.6, 131.4, and 95.6 mAh · g-1 at 0.1, 1.0, and 5.0 C, respectively. Excellent cycling performance with a discharge capacity of 80.9 mAh · g-1 is still maintained even at a high current density of 10 °C. Our results suggest that this material is a promising cathode material for lithium ion batteries

LCST-Type Phase Behavior of Aqueous Biphasic Systems Composed of Phosphonium-Based Ionic Liquids and Potassium Phosphate

Although aqueous biphasic systems (ABS) composed of phosphonium-based ionic liquids (ILs) have demonstrated superior performance as viable media for biocompatible extraction processes, the formation of ABS with tetrabutylphosphonium trifluoroacetate ([P₄₄₄₄]­CF₃COO) and tributyloctylphosphonium bromide ([P₄₄₄₈]­Br) were seldom investigated. To evaluate the hydrophilicity of [P₄₄₄₄]­CF₃COO and [P₄₄₄₈]­Br, the solubility curves for the mixture of the ILs and water were determined. The influence of temperature on the phase behavior for the [P₄₄₄₈]­Br + K₃PO₄ + H₂O and [P₄₄₄₄] CF₃COO + K₃PO₄ + H₂O systems were also obtained. Results show that both [P₄₄₄₈]Br + H₂O and [P₄₄₄₄]­CF₃COO + H₂O binary systems undergo lower critical solution temperature-type phase transition, and the ABS composed of [P₄₄₄₄]­CF₃COO and [P₄₄₄₈]Br can be formed with K₃PO₄ at a wide range of temperatures. Moreover, the ABS are highly temperature dependent, and biphasic region expands with an increase in temperature. On the other hand, the capability of the ILs to induce ABS follows the tread [P₄₄₄₈]Br > [P₄₄₄₄]­CF₃COO. This suggests that the lower cation hydrophilicity of ILs results in greater biphasic area in the ABS formed by phosphonium-based ILs

Biomarkers and the related global change

Biomarkers occurring at a variety of geological environments have been shown to be effective in the exploitation of past global changes. Reconstructions of paleo vegetation, paleo temperature, paleo precipitation, paleo atmospheric CO2 concentration, paleo monsoon were conducted in various sediments using the distributions and isotopic compositions of biomarkers. Here we present a 40cm long peat record based on the analyses of biomarker distribution and compound specific δ D using gas chromatography (GC), GC mass spectrometry (MS) and GC thermal conversion isotope ratio MS. The preliminary result shows that both the distribution and isotope composition of biomarkers are capable of reflecting the variations of paleoclimate. Specifically, The profile trends of the carbon preference index of n alkanoic acids, the ratio of C 23 /C31 n alkanes and δ D of C23 n alkane are comparable with the documented temperature record of the period the peat core spans