Semisupervised ranking on very large graphs with rich metadata

ABSTRACT Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking scores by only resorting to graph structure information. A natural question arises here, that is, how to effectively and efficiently leverage all the information to more accurately calculate graph ranking scores than the conventional algorithms, assuming that the graph is also very large. Previous work only partially tackled the problem, and the proposed solutions are also not satisfying. This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking. Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank. In the algorithm, the objective function is defined based upon a Markov random walk on the graph. The transition probability and the reset probability of the Markov model are defined as parametric models based on features on nodes and edges. By minimizing the objective function, subject to a number of constraints derived from supervision information, we simultaneously learn the optimal parameters of the model and the optimal ranking scores of the nodes. Finally, we show that it is possible to make the algorithm efficient to * This work was performed when the third author was an intern at Microsoft Research Asia. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. handle a billion-node graph by taking advantage of the sparsity of the graph and implement it in the MapReduce logic. Experiments on real data from a commercial search engine show that the proposed algorithm can outperform previous algorithms on several tasks

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for humans with each record measuring 20,000 genes. This highlights the need for unsupervised representation learning to fully ingest these data, yet classical transformer architectures are prohibitive to train on such data in terms of both computation and memory. To address this challenge, we propose a novel asymmetric encoder-decoder transformer for scRNA-seq data, called xTrimoGene$^\alpha$ (or xTrimoGene for short), which leverages the sparse characteristic of the data to scale up the pre-training. This scalable design of xTrimoGene reduces FLOPs by one to two orders of magnitude compared to classical transformers while maintaining high accuracy, enabling us to train the largest transformer models over the largest scRNA-seq dataset today. Our experiments also show that the performance of xTrimoGene improves as we scale up the model sizes, and it also leads to SOTA performance over various downstream tasks, such as cell type annotation, perturb-seq effect prediction, and drug combination prediction. xTrimoGene model is now available for use as a service via the following link: https://api.biomap.com/xTrimoGene/apply.Comment: Accepted by NeurIPS 202

Bulk and Surface Contributions to Ionisation Potentials of Metal Oxides

Determining the absolute band edge positions in solid materials is crucial for optimising their performance in wide-ranging applications including photocatalysis and electronic devices. However, obtaining absolute energies is challenging, as seen in CeO2, where experimental measurements show substantial discrepancies in the ionisation potential (IP). Here, we have combined several theoretical approaches, from classical electrostatics to quantum mechanics, to elucidate the bulk and surface contributions to the IP of metal oxides. We have determined a theoretical bulk contribution to the IP of stoichiometric CeO2 of only 5.38 eV, while surface orientation results in intrinsic IP variations from 4.2 eV to 8.2 eV. Highly tuneable IPs were also found in TiO2, ZrO2, and HfO2, in which surface polarisation plays a pivotal role in long-range energy level shifting. Our analysis, in addition to rationalising the observed range of experimental results, provides a firm basis for future interpretations of experimental and computational studies of oxide band structures

A Bi2Te3-Filled Nickel Foam Film with Exceptional Flexibility and Thermoelectric Performance

The past decades have witnessed surging demand for wearable electronics, for which thermoelectrics (TEs) are considered a promising self-charging technology, as they are capable of converting skin heat into electricity directly. Bi2Te3 is the most-used TE material at room temperature, due to a high zT of ~1. However, it is different to integrate Bi2Te3 for wearable TEs owing to its intrinsic rigidity. Bi2Te3 could be flexible when made thin enough, but this implies a small electrical and thermal load, thus severely restricting the power output. Herein, we developed a Bi2Te3/nickel foam (NiFoam) composite film through solvothermal deposition of Bi2Te3 nanoplates into porous NiFoam. Due to the mesh structure and ductility of Ni Foam, the film, with a thickness of 160 μm, exhibited a high figure of merit for flexibility, 0.016, connoting higher output. Moreover, the film also revealed a high tensile strength of 12.7 ± 0.04 MPa and a maximum elongation rate of 28.8%. In addition, due to the film’s high electrical conductivity and enhanced Seebeck coefficient, an outstanding power factor of 850 μW m−1 K−2 was achieved, which is among the highest ever reported. A module fabricated with five such n-type legs integrated electrically in series and thermally in parallel showed an output power of 22.8 nW at a temperature gap of 30 K. This work offered a cost-effective avenue for making highly flexible TE films for power supply of wearable electronics by intercalating TE nanoplates into porous and meshed-structure materials

Bulk and surface contributions to ionisation potentials of metal oxides

Determining the absolute band edge positions in solid materials is crucial for optimising their performance in wide‐ranging applications including photocatalysis and electronic devices. However, obtaining absolute energies is challenging, as seen in CeO2, where experimental measurements show substantial discrepancies in the ionisation potential (IP). Here, we have combined several theoretical approaches, from classical electrostatics to quantum mechanics, to elucidate the bulk and surface contributions to the IP of metal oxides. We have determined a theoretical bulk contribution to the IP of stoichiometric CeO2 of only 5.38 eV, while surface orientation results in intrinsic IP variations ranging from 4.2 eV to 8.2 eV. Highly tuneable IPs were also found in TiO2, ZrO2, and HfO2, in which surface polarisation plays a pivotal role in long‐range energy level shifting. Our analysis, in addition to rationalising the observed range of experimental results, provides a firm basis for future interpretations of experimental and computational studies of oxide band structures

Bulk and surface contributions to ionisation potentials of metal oxides

Determining the absolute band edge positions in solid materials is crucial for optimising their performance in wide‐ranging applications including photocatalysis and electronic devices. However, obtaining absolute energies is challenging, as seen in CeO2, where experimental measurements show substantial discrepancies in the ionisation potential (IP). Here, we have combined several theoretical approaches, from classical electrostatics to quantum mechanics, to elucidate the bulk and surface contributions to the IP of metal oxides. We have determined a theoretical bulk contribution to the IP of stoichiometric CeO2 of only 5.38 eV, while surface orientation results in intrinsic IP variations ranging from 4.2 eV to 8.2 eV. Highly tuneable IPs were also found in TiO2, ZrO2, and HfO2, in which surface polarisation plays a pivotal role in long‐range energy level shifting. Our analysis, in addition to rationalising the observed range of experimental results, provides a firm basis for future interpretations of experimental and computational studies of oxide band structures