Ordering-sensitive and Semantic-aware Topic Modeling

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.Comment: To appear in proceedings of AAAI 201

Design automation of sustainable self-compacting concrete containing fly ash via data driven performance prediction

Self-compacting concrete (SCC) is a highly flowable and segregation-resistant material, effectively facilitating proper filling and ensuring exceptional structural performance in confined spaces. Incorporating fly ash as a supplementary cementitious material in SCC mixtures yields numerous benefits, including enhanced cost-effectiveness in construction and the advancement of environmental sustainability. Nevertheless, the addition of fly ash in SCC poses significant challenges in modelling and predicting the properties of SCC due to lack of understanding of its influence on material rheology and bonding. It is therefore desirable to develop more appropriate machine learning approach to compliment the large scale and costly laboratory-based experiments. This paper presents four well trained supervised machine learning models for the prediction of fresh and hardened properties of SCC containing fly ash: support vector machine (SVM), decision tree, random forest, and artificial neural network (ANN). Training datasets gathered from publicly available existing relevant literature, were analysed and processed prior to shape the required machine learning models. Optimization strategies of hyperparameters were also implemented for each model. To evaluate the performance of these machine learning models and to compare their accuracy, regression error characteristic curves and Taylor diagrams were utilized. The findings reveal that all models demonstrate promising results, with the random forest model outperforming the others in predicting SCC properties with higher accuracy. This underscores the potential of random forest algorithms in accurately modelling and predicting the properties of fly ash-infused SCC. Finally, a data driven implementation framework has been developed, thereby offering robust and logical strategy for experimental designs and guidance for developing sustainable SCC

Influence of sand to aggregate ratio on the fresh and mechanical properties of self-compacting high-performance concrete

Self-compacting high-performance concrete (SCHPC) combines the properties and advantages of self-compacting concrete and high-performance concrete in both fresh and hardened state. For the SCHPC mix design, sand to aggregate ratio is a crucial parameter and plays an important role in governing the properties of SCHPC mix. This paper presents the results of an experimental investigation on the flowability, passing ability and mechanical properties of SCHPC mixes for various sand to total aggregate (S/A) ratio and water to cementitious material (w/cm) ratio. Tests were conducted on specimens using four (w/cm) ratios: 0.26, 0.30, 0.35 and 0.40 and two(S/A) ratios: 48% and 53%. All the mixtures were tested using slump flow test, J-Ring test, and L-box test in the fresh state as well as compressive strength, splitting tensile strength, and unit weight in the hardened state. The test results revealed that a lower S/A ratio (0.48) enhanced the flowability where as the higher S/A ratio (0.53) enhanced the passing ability. The lower S/A ratio ( 0.48), containing greater proportion of coarse aggregate, generally improved the mechanical properties of SCHPC compared to the mixes with the higher S/A ratio (0.53)

Effect of sand to aggregate ratio on the properties of self-compacting high-performance concrete

Self-compacting high-performance concrete (SCHPC) combines the properties and advantages of self-compacting concrete and high-performance concrete in both fresh and hardened states. For the SCHPC mix design, sand to aggregate ratio is a crucial parameter and plays an important role in governing the properties of SCHPC mix. This paper presents the results of an experimental investigation on the flowability, passing ability and mechanical properties of SCHPC mixes for various sand to total aggregate (S/A) ratio and water to cementitious material (w/cm) ratio. Tests were conducted on specimens using four (w/cm) ratios: 0.26, 0.30, 0.35 and 0.40 and two (S/A) ratios: 48% and 53%. All the mixtures were tested using slump flow test, J-Ring test, and L-box test in the fresh state as well as compressive strength, splitting tensile strength, andun it weight in the hardened state. The test results revealed that a lower S/A ratio (0.48) enhanced the flowability whereas the higher S/A ratio (0.53) enhanced the passing ability. The lower S/A ratio (0.48), containing greater proportion of coarse aggregate, generally improved the mechanical properties of SCHPC compared to the mixes with the higher S/A ratio (0.53

In Situ Focused Ion Beam Scanning Electron Microscope Study of Microstructural Evolution of Single Tin Particle Anode for Li-Ion Batteries

Tin (Sn) is a potential anode material for highenergy density Li-ion batteries because of its high capacity, safety, abundance and low cost. However, Sn suffers from large volume change during cycling, leading to fast degradation of the electrode. For the first time, the microstructural evolution of micrometer-sized single Sn particle was monitored by focused-ion beam (FIB) polishing and scanning electron microscopy (SEM) imaging during electrochemical cycling by in situ FIB-SEM. Our results show the formation and evolution of cracks during lithiation, evolution of porous structure during delithiation and volume expansion/contraction during cycling. The electrochemical performance and the microstructural evolution of the Sn microparticle during cycling are directly correlated, which provides insights for understanding Sn-based electrode materials

Selenium Nanocomposite Cathode with Long Cycle Life for Rechargeable Li-Se Batteries

Selenium (Se) is a potential cathode material for high energy density rechargeable lithium batteries. In this study, a binder‐free Se‐carbon nanotube (CNT) composite electrode has been prepared by a facile chemical method. At initial state, Se is present in the form of branched nanowires with a diameter of <150 nm and a length of 1–2 μm, interwoven with CNTs. After discharge and re‐charge, the Se nanowires are converted to nanoparticles embedded in the CNT network. This synthesis method provides a path for fabricating the Se cathodes with controllable mass loading and thickness. By studying the composite electrodes with different Se loading and thickness, we found that the electrode thickness has a critical impact on the distribution of Se during repeated cycling. Promising cycling performance was achieved in thin electrodes with high Se loading. The composite electrode with 23 μm thickness and 60 % Se loading shows a high initial capacity of 537 mAh g−1 and stable cycling performance with a capacity of 401 mAh g−1 after 500 cycles at 1 C rate. This study reports a synthesis strategy to obtain Se/CNT composite cathode with long cycle life for rechargeable Li−Se batteries

Feedback-driven anisotropy in the circumgalactic medium for quenching galaxies in the SIMBA simulations

We use the SIMBA galaxy formation simulation suite to explore anisotropies in the properties of circumgalactic gas that resultfrom accretion and feedback processes. We particularly focus on the impact of bipolar active galactic nuclei (AGNs) jet feedbackas implemented in SIMBA , which quenches galaxies and has a dramatic effect on large-scale gas properties. We show that jetfeedback at low redshifts is most common in the stellar mass range (1–5) × 1010 M, so we focus on galaxies with active jets inthis mass range. In comparison to runs without jet feedback, jets cause lower densities and higher temperatures along the galaxyminor axis (SIMBA jet direction) at radii 0.5r200c − 4r200c and beyond. This effect is less apparent at higher or lower stellarmasses, and is strongest within green valley galaxies. The metallicity also shows strong anisotropy out to large scales, drivenby star formation feedback. We find substantially stronger anisotropy at 0.5r200c, but this also exists in runs with no explicitfeedback, suggesting that it is due to anisotropic accretion. Finally, we explore anisotropy in the bulk radial motion of the gas,finding that both star formation and AGN wind feedback contribute to pushing the gas outwards along the minor axis at 1Mpc, but AGN jet feedback further causes bulk outflow along the minor axis out to several Mpc, which drives quenching viagas starvation. These results provide observational signatures for the operation of AGN feedback in galaxy quenching

Feedback-driven anisotropy in the circumgalactic medium for quenching galaxies in the SIMBA simulations

We use the SIMBA galaxy formation simulation suite to explore anisotropies in the properties of circumgalactic gas that result from accretion and feedback processes. We particularly focus on the impact of bipolar active galactic nuclei (AGN) jet feedback as implemented in SIMBA, which quenches galaxies and has a dramatic effect on large-scale gas properties. We show that jet feedback at low redshifts is most common in the stellar mass range $(1-5)\times 10^{10}M_\odot$, so we focus on galaxies with active jets in this mass range. In comparison to runs without jet feedback, jets cause lower densities and higher temperatures along the galaxy minor axis (SIMBA jet direction) at radii >=$0.5r_{200c}-4r_{200c}$ and beyond. This effect is less apparent at higher or lower stellar masses, and is strongest within green valley galaxies. The metallicity also shows strong anisotropy out to large scales, driven by star formation feedback. We find substantially stronger anisotropy at <=$0.5r_{200c}$, but this also exists in runs with no explicit feedback, suggesting that it is due to anisotropic accretion. Finally, we explore anisotropy in the bulk radial motion of the gas, finding that both star formation and AGN wind feedback contribute to pushing the gas outwards along the minor axis at <=1 Mpc, but AGN jet feedback further causes bulk outflow along the minor axis out to several Mpc, which drives quenching via gas starvation. These results provide observational signatures for the operation of AGN feedback in galaxy quenching.Comment: 21 pages, 14 figures, 2 tables, accepted by MNRAS. Comments are welcome

Electrochemical behavior of tin foil anode in half cell and full cell with sulfur cathode

Tin-based (Sn) metal anode has been considered an attractive candidate for rechargeable lithium batteries due to its high specific capacity, safety and low cost. However, the large volume change of Sn during cycling leads to rapid capacity decay. To address this issue, Sn foil was used as a high capacity anode by controlling the degree of lithium uptake. We studied the electrochemical behavior of Sn foil anode in half cell and full cell with sulfur cathode, including phase transform, morphological change, discharge/charge profiles and cycling performance. Enhanced cycling performance has been achieved by limiting the lithiation capacity of the Sn foil electrode. A full cell consisting of a pre-lithiated Sn foil anode and a sulfur cathode was constructed and tested. The full cell exhibits an initial capacity of 1142 mAh g−1 (based on the sulfur mass in the cathode), followed by stable cycling performance with a capacity retention of 550 mAh g−1 after 100 cycles at C/2 rate. This study reports a potential prospect to utilize Sn and S as a combination in rechargeable lithium batteries