Deep Semantic Role Labeling with Self-Attention

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. In this paper, we present a simple and effective architecture for SRL which aims to address these problems. Our model is based on self-attention which can directly capture the relationships between two tokens regardless of their distance. Our single model achieves F$_1=83.4$ on the CoNLL-2005 shared task dataset and F$_1=82.7$ on the CoNLL-2012 shared task dataset, which outperforms the previous state-of-the-art results by $1.8$ and $1.0$ F$_1$ score respectively. Besides, our model is computationally efficient, and the parsing speed is 50K tokens per second on a single Titan X GPU.Comment: Accepted by AAAI-201

Using 3D seismic exploration to detect ground fissure

   As a kind of supergene geological phenomenon, ground fissure has brought great inconvenience to human life. In addition, it also has a close relationship with earthquake. However, it is very difficult to ascertain the extension depth of ground fissure since its concealment and uncertainty. In this paper, 3D seismic exploration is used to detect ground fissure in Shanxi Province of China. Specific parameters for seismic data acquisition, processing and interpretation are analysed. Firstly, seismic data acquisition method and its corresponding parameters are discussed. Small dose explosive sources and high frequency geophones are used. Small trace interval and appropriate fold are also adopted. Secondly, seismic data processing is processed from shot record to seismic profile. Multi-domain loop iteration de-noising is used to get high signal-to-noise ratio data. Accurate near surface model, interactive iteration and residual static correction are used to eliminate the impact of low velocity zone and the static correction problem. Large common middle point bin and small velocity analysis interval are used for high accuracy velocity spectrum analysis. The mute parameter of stretching distortion and the migration aperture are researched for shallow ground fissure detection. Thirdly, seismic data interpretation is processed to get ground fissure distribution. Fault enhanced filter is used to improve the signal-to-noise ratio effectively and the chimney cube is used to identify ground fissure automatically. Thus, the specific 3D seismic exploration method used in this paper is suitable for ground fissure detection.Cited as: Shi, S., Liu, Z., Feng, J., Feng, G., Li, M. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19, doi: 10.26804/ager.2020.01.0

Biomass Gasification: An Overview of Technological Barriers and Socio-Environmental Impact

Biomass gasification has been regarded as a promising technology to utilize bioenergy sustainably. However, further exploitation of biomass gasification still needs to overcome a significant number of technological and logistic challenges. In this chapter, the current development status of biomass gasification, especially for the activities in China, has been presented. The biomass characters and the challenges associated with biomass collection and transportation are covered and it is believed that biomass gasification coupled with distributed power generation will be more competitive in some small communities with large amount of local biomass materials. The technical part of biomass gasification is detailed by introducing different types of gasifiers as well as investigating the minimization methods of tar, which have become more and more important. In fact, applying biomass gasification also needs to deal with other socio-environmental barriers, such as health concerns, environmental issues and public fears. However, an objective financial return can actually accelerate the commercialization of biomass gasification for power and heat generation, and in the meantime, it will also contribute to other technical breakthroughs

Design and Analysis of the Thermal Management System of a Hybrid Turboelectric Regional Jet for the NASA ULI

Presented at AIAA/IEEE Electric Aircraft Technologies Symposium 2020A team of researchers from multiple universities are collaborating on the demonstration of a hybrid turboelectric regional jet for 2030 under the NASA ULI Program. The thermal management is one of the major challenges for the development of such an electric propulsion concept. Existing studies hardly modeled the thermal management systems with the propulsion systems nor integrated it to the aircraft for system- and mission-level analyses. Therefore, it is very difficult to verify whether a design of the thermal management system is feasible and optimal based on current literature. To fill this gap, this paper presents a design of the thermal management system for the hybrid turboelectric regional jet under the ULI program and integrates it to the aircraft. The TMS is tested against the cooling requirements, where the thermal loads from the electric propulsion system are quantified through the whole mission. Potential solutions for peak thermal loads during takeoff and climb are also proposed and analyzed, where additional coolant or phase change materials are used. Moreover, the impacts of the TMS on the system- and mission-level performance are investigated by the presented integration approach as well. It is discovered that a basic oil-air thermal management system cannot fully remove the heat during the early mission segments. Using additional coolant or phase change materials as heat absorption can handle such heating problem, but penalty due to additional weight is added. It is found that greater penalties in fuel burn and takeoff weight are added by additional coolant solution than the phase change material solution.NASA, GR1000571

SGDP: A Stream-Graph Neural Network Based Data Prefetcher

Data prefetching is important for storage system optimization and access performance improvement. Traditional prefetchers work well for mining access patterns of sequential logical block address (LBA) but cannot handle complex non-sequential patterns that commonly exist in real-world applications. The state-of-the-art (SOTA) learning-based prefetchers cover more LBA accesses. However, they do not adequately consider the spatial interdependencies between LBA deltas, which leads to limited performance and robustness. This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP). Specifically, SGDP models LBA delta streams using a weighted directed graph structure to represent interactive relations among LBA deltas and further extracts hybrid features by graph neural networks for data prefetching. We conduct extensive experiments on eight real-world datasets. Empirical results verify that SGDP outperforms the SOTA methods in terms of the hit ratio by 6.21%, the effective prefetching ratio by 7.00%, and speeds up inference time by 3.13X on average. Besides, we generalize SGDP to different variants by different stream constructions, further expanding its application scenarios and demonstrating its robustness. SGDP offers a novel data prefetching solution and has been verified in commercial hybrid storage systems in the experimental phase. Our codes and appendix are available at https://github.com/yyysjz1997/SGDP/

EDA-Driven Preprocessing for SAT Solving

Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the limitations of existing methods, our Electronic Design Automation (EDA)-driven preprocessing framework introduces a novel methodology for preparing SAT instances, leveraging both circuit and CNF formats for enhanced flexibility and efficiency. Central to our approach is the integration of a new logic synthesis technique, guided by a reinforcement learning agent, and a novel cost-customized LUT mapping strategy, enabling efficient handling of diverse SAT challenges. By transforming the SAT competition benchmarks into circuit instances, our framework demonstrates substantial performance improvements, as evidenced by a 52.42% reduction on average compared to solving directly. Moreover, our framework achieves a remarkable 96.14% runtime reduction on average for a set of logic equivalence checking problems that exhibit inherent circuit structures. These results highlight the effectiveness and versatility of our approach in handling both CNF and circuit instances. The code is available at https://github.com/cure-lab/EDA4SAT

Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same time, the generalized tensor Autoregressive Integrated Moving Average (ARIMA) is explicitly used on consecutive core tensors to predict future samples. In this manner, the proposed approach tactically incorporates the unique advantages of MDT tensorization (to exploit mutual correlations) and tensor ARIMA coupled with low-rank Tucker decomposition into a unified framework. This framework exploits the low-rank structure of block Hankel tensors in the embedded space and captures the intrinsic correlations among multiple TS, which thus can improve the forecasting results, especially for multiple short time series. Experiments conducted on three public datasets and two industrial datasets verify that the proposed BHT-ARIMA effectively improves forecasting accuracy and reduces computational cost compared with the state-of-the-art methods.Comment: Accepted by AAAI 202

SATformer: Transformer-Based UNSAT Core Learning

This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the problem from the opposite direction by focusing on unsatisfiability. Specifically, it models clause interactions to identify any unsatisfiable sub-problems. Using a graph neural network, we convert clauses into clause embeddings and employ a hierarchical Transformer-based model to understand clause correlation. SATformer is trained through a multi-task learning approach, using the single-bit satisfiability result and the minimal unsatisfiable core (MUC) for UNSAT problems as clause supervision. As an end-to-end learning-based satisfiability classifier, the performance of SATformer surpasses that of NeuroSAT significantly. Furthermore, we integrate the clause predictions made by SATformer into modern heuristic-based SAT solvers and validate our approach with a logic equivalence checking task. Experimental results show that our SATformer can decrease the runtime of existing solvers by an average of 21.33%

Curdlan Prevents the Cognitive Deficits Induced by a High-Fat Diet in Mice via the Gut-Brain Axis

A high-fat (HF) diet is a major predisposing factor of neuroinflammation and cognitive deficits. Recently, changes in the gut microbiota have been associated with neuroinflammation and cognitive impairment, through the gut-brain axis. Curdlan, a bacterial polysaccharide widely used as food additive, has the potential to alter the composition of the microbiota and improve the gut-brain axis. However, the effects of curdlan against HF diet-induced neuroinflammation and cognitive decline have not been investigated. We aimed to evaluate the neuroprotective effect and mechanism of dietary curdlan supplementation against the obesity-associated cognitive decline observed in mice fed a HF diet. C57Bl/6J male mice were fed with either a control, HF, or HF with curdlan supplementation diets for 7 days (acute) or 15 weeks (chronic). We found that acute curdlan supplementation prevented the gut microbial composition shift induced by HF diet. Chronic curdlan supplementation prevented cognitive declines induced by HF diet. In addition, curdlan protected against the HF diet-induced abnormities in colonic permeability, hyperendotoxemia, and colonic inflammation. Furthermore, in the prefrontal cortex (PFC) and hippocampus, curdlan mitigated microgliosis, neuroinflammation, and synaptic impairments induced by a HF diet. Thus, curdlan-as a food additive and prebiotic-can prevent cognitive deficits induced by HF diet via the colon-brain axis

Ampere-hour-scale soft-package potassium-ion hybrid capacitors enabling 6-minute fast-charging

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices targeting charging times of less than 10 minutes are desired to increase widespread adoption. However, this metric is difficult to achieve in conventional Li-ion batteries due to their inherent reaction mechanism and safety hazards at high current densities. In this work, we report 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs), which combine the merits of high-energy density of battery-type negative electrodes and high-power density of capacitor-type positive electrodes. The PIHC consists of a defect-rich, high specific surface area N-doped carbon nanotube-based positive electrode, MnO quantum dots inlaid spacing-expanded carbon nanotube-based negative electrode, carbonate-based non-aqueous electrolyte, and a binder- and current collector-free cell design. Through the optimization of the cell configuration, electrodes, and electrolyte, the full cells (1 Ah) exhibit a cell voltage up to 4.8 V, high full-cell level specific energy of 140 Wh kg-1 (based on the whole mass of device) with a full charge of 6 minutes. An 88% capacity retention after 200 cycles at 10 C (10 A) and a voltage retention of 99% at 25 ± 1 °C are also demonstrated