Online Influence Maximization with Node-level Feedback Using Standard Offline Oracles

We study the online influence maximization (OIM) problem in social networks, where in multiple rounds the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade. We focus on two major challenges in this paper. First, we work with node-level feedback instead of edge-level feedback. The edge-level feedback reveals all edges that pass through information in a cascade, where the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to observe who is influenced but very difficult to observe from which relationship (edge) the influence comes from. Second, we use standard offline oracle instead of offline pair-oracle. To compute a good seed set for the next round, an offline pair-oracle finds the best seed set and the best parameters within the confidence region simultaneously, and such an oracle is difficult to compute due to the combinatorial core of OIM problem. So we focus on how to use the standard offline influence maximization oracle which finds the best seed set given the edge parameters as input. In this paper, we resolve these challenges for the two most popular diffusion models, the independent cascade (IC) and the linear threshold (LT) model. For the IC model, the past research only achieves edge-level feedback, while we present the first $widetilde{O}(sqrt{T})$-regret algorithm for the node-level feedback. Besides, the algorithm only invokes standard offline oracles. For the LT model, a recent study only provides an OIM solution that meets the first challenge but still requires a pair-oracle. In this paper, we apply a similar technique as in the IC model to replace the pair-oracle with a standard oracle while maintaining $widetilde{O}(sqrt{T})$-regret.

Ohm-GPU: Integrating New Optical Network and Heterogeneous Memory into GPU Multi-Processors

Traditional graphics processing units (GPUs) suffer from the low memory capacity and demand for high memory bandwidth. To address these challenges, we propose Ohm-GPU, a new optical network based heterogeneous memory design for GPUs. Specifically, Ohm-GPU can expand the memory capacity by combing a set of high-density 3D XPoint and DRAM modules as heterogeneous memory. To prevent memory channels from throttling throughput of GPU memory system, Ohm-GPU replaces the electrical lanes in the traditional memory channel with a high-performance optical network. However, the hybrid memory can introduce frequent data migrations between DRAM and 3D XPoint, which can unfortunately occupy the memory channel and increase the optical network traffic. To prevent the intensive data migrations from blocking normal memory services, Ohm-GPU revises the existing memory controller and designs a new optical network infrastructure, which enables the memory channel to serve the data migrations and memory requests, in parallel. Our evaluation results reveal that Ohm-GPU can improve the performance by 181% and 27%, compared to a DRAM-based GPU memory system and the baseline optical network based heterogeneous memory system, respectively.

Detection of pairwise kSZ effect with DESI galaxy clusters and Planck

We report a $5sigma$ detection of the pairwise kinematic Sunyaev-Zeldovich (kSZ) effect, combining galaxy clusters in DESI imaging surveys and the Planck temperature maps. The detection is facilitated by both improvements in the data and in the analysis method. For the data, we adopt the recently released galaxy group catalog (Y20: cite{yang2020extended}) with $sim 10^6$ robustly-identified groups, and construct various galaxy cluster samples for the kSZ measurement. The Y20 catalogue also provides estimation of halo mass, which further improves the kSZ measurement by $sim 10%$. For the analysis method, we derive an optimal estimator of pairwise kSZ through the maximum likelihood analysis. It also handles potential systematic errors self-consistently. The baseline cluster sample, containing the $1.2times 10^5$ richest galaxy clusters of typical mass ~$ 10^{14} M_{odot}/h$ at typical redshift $0.2$-$0.5$, rules out the null hypothesis at $5sigma$. When fitting with a pairwise kSZ template from simulations, the signal is detected at $4.7sigma$ and the average optical depth is constrained as $bar{tau}_e=(1.66pm 0.35)times 10^{-4}$. We perform various internal checks, with different cluster selection criteria, different sky coverage and redshift range, different CMB maps, different filter sizes, different treatments of potential systematics and the covariance matrix. The kSZ effect is consistently detected with $2.5leq $S/N$leq 5.6$ and acceptable $chi^2_{rm min}$, across a variety of cluster samples. The S/N is limited by both the Planck resolution and the photo-z accuracy, and therefore can be significant improved with DESI spectroscopic redshift information and with other CMB experiments.

Probing the time variation of fine structure constant using galaxy clusters and quintessence model

We explore a possible time variation of the fine structure constant ($alpha equiv e^2/hbar c$) using the Sunyaev-Zeldovich effect measurements of galaxy clusters along with their X-ray observations. Specifically, the ratio of the integrated Compto-ionization parameter $Y_{SZ}D_A^2$ and its X-ray counterpart $Y_X$ is used as an observable to constrain the bounds on the variation of $alpha$. Considering the violation of cosmic distance duality relation, this ratio depends on the fine structure constant as $sim alpha^3$. We use the quintessence model to provide the origin of $alpha$ time variation. In order to give a robust test on $alpha$ variation, two galaxy cluster samples, the 61 clusters provided by the Planck collaboration and the 58 clusters detected by the South Pole Telescope, are collected for analysis. Their X-ray observations are given by the XMM-Newton survey. Our results give $zeta=-0.203^{+0.101}_{-0.099}$ for the Planck sample and $zeta=-0.043^{+0.165}_{-0.148}$ for the SPT sample, indicating that $alpha$ is constant with redshift within $3sigma$ and $1sigma$ for the two samples, respectively.

Realization of A Non-Markov Chain in A Single 2D Crystal RRAM

The non-Markov processes widely exist in thermodymanic processes, while it usually requires packing of many transistors and memories with great system complexity in traditional device architecture to minic such functions. Two-dimensional (2D) material-based resistive random access memory (RRAM) devices show potential for next-generation computing systems with much-reduced complexity. Here, we achieve the non-Markov chain in an individual RRAM device based on 2D mica with a vertical metal/mica/metal structure. We find that the internal potassium ions (K+) in 2D mica gradually move along the direction of the applied electric field, making the initially insulating mica conductive. The accumulation of K+ is tuned by electrical field, and the 2D-mica RRAM possesses both unipolar and bipolar memory windows, high on/off ratio, decent stability and repeatability.Importantly, the non-Markov chain algorithm is established for the first time in a single RRAM, in which the movement of K+ is dependent on the stimulated voltage as well as their past states. This work not only uncovers the inner ionic conductivity of 2D mica, but also opens the door for such novel RRAM devices with numerous functions and applications.

Out-of-Plane Resistance Switching of 2D Bi2O2Se at Nanoscale

2D bismuth oxyselenide (Bi2O2Se) with high electron mobility shows great potential for nanoelectronics. Although in-plane properties of Bi2O2Se have been widely studied, its out-ofplane electrical transport behavior remains elusive, despite its importance in fabricating devices with new functionality and high integration density. Here, we study the out-of-plane electrical properties of 2D Bi2O2Se at nanoscale by conductive atomic force microscope. We find that hillocks with tunable heights and sizes are formed on Bi2O2Se after applying vertical electrical field. Intriguingly, such hillocks are conductive in vertical direction, resulting in a previously unknown out-of-plane resistance switching in thick Bi2O2Se flakes while ohmic conductive characteristic in thin ones. Furthermore, we observe the transformation from bipolar to stable unipolar conduction in thick Bi2O2Se flake possessing such hillocks, suggesting its potential to function as a selector in vertical devices. Our work reveals unique out-of-plane transport behavior of 2D Bi2O2Se, providing the basis for fabricating vertical devices based on this emerging 2D material.

Photonic quantum computation by chiral quantun networks

Inspired by the excellent control of single photons realized by the atom-photon-chiral couplings, we propose a novel potential photonic-quantum-computation scheme. The single-photon rotating and phase-shift operations, which can be controlled by another single photon, are realized by properly designed atom-photon-chiral couplings. The operations can be integrated into a chiral quantum network to realize photonic quantum computation. Based on the proposal, an algorithm to perform the machine learning tasks is developed, in which the essential nonlinearities come from the appropriately designed operations.

Communication-Computation Efficient Device-Edge Co-Inference via AutoML

Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications. To accelerate the inference process, on-device model sparsification and intermediate feature compression are regarded as two prominent techniques. However, as the on-device model sparsity level and intermediate feature compression ratio have direct impacts on computation workload and communication overhead respectively, and both of them affect the inference accuracy, finding the optimal values of these hyper-parameters brings a major challenge due to the large search space. In this paper, we endeavor to develop an efficient algorithm to determine these hyper-parameters. By selecting a suitable model split point and a pair of encoder/decoder for the intermediate feature vector, this problem is casted as a sequential decision problem, for which, a novel automated machine learning (AutoML) framework is proposed based on deep reinforcement learning (DRL). Experiment results on an image classification task demonstrate the effectiveness of the proposed framework in achieving a better communication-computation trade-off and significant inference speedup against various baseline schemes.

Genetic Programming for Manifold Learning: Preserving Local Topology

Manifold learning methods are an invaluable tool in todays world of increasingly huge datasets. Manifold learning algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through non-linear transformations that preserve the most important structure of the original data. State-of-the-art manifold learning methods directly optimise an embedding without mapping between the original space and the discovered embedded space. This makes interpretability - a key requirement in exploratory data analysis - nearly impossible. Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding. However, genetic programming-based manifold learning has struggled to match the performance of other approaches. In this work, we propose a new approach to using genetic programming for manifold learning, which preserves local topology. This is expected to significantly improve performance on tasks where local neighbourhood structure (topology) is paramount. We compare our proposed approach with various baseline manifold learning methods and find that it often outperforms other methods, including a clear improvement over previous genetic programming approaches. These results are particularly promising, given the potential interpretability and reusability of the evolved mappings.

Blockchain Phishing Scam Detection via Multi-channel Graph Classification

With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem. Usually, the existing works define phishing scam detection as a node classification task by learning the potential features of users through graph embedding methods such as random walk or graph neural network (GNN). However, these detection methods are suffered from high complexity due to the large scale of the blockchain transaction network, ignoring temporal information of the transaction. Addressing this problem, we defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task. To extract richer information from the input graph, we proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN. The transaction pattern graphs and MCGC are more able to detect potential phishing scammers by extracting the transaction pattern features of the target users. Extensive experiments on seven benchmark and Ethereum datasets demonstrate that the proposed MCGC can not only achieve state-of-the-art performance in the graph classification task but also achieve effective phishing scam detection based on the target users transaction pattern graphs.