A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models

Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the success of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and further provide the theoretically optimal tuning parameters specification to achieve faster convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results.Comment: 45 pages, 5 figure

Trace Amounts of Triple-Functional Additives Enable Reversible Aqueous Zinc-Ion Batteries from a Comprehensive Perspective

Although their cost-effectiveness and intrinsic safety, aqueous zinc-ion batteries suffer from notorious side reactions including hydrogen evolution reaction, Zn corrosion and passivation, and Zn dendrite formation on the anode. Despite numerous strategies to alleviate these side reactions have been demonstrated, they can only provide limited performance improvement from a single aspect. Herein, a triple-functional additive with trace amounts, ammonium hydroxide, was demonstrated to comprehensively protect zinc anodes. The results show that the shift of electrolyte pH from 4.1 to 5.2 lowers the HER potential and encourages the in situ formation of a uniform ZHS-based solid electrolyte interphase on Zn anodes. Moreover, cationic NH4+ can preferentially adsorb on the Zn anode surface to shield the "tip effect" and homogenize the electric field. Benefitting from this comprehensive protection, dendrite-free Zn deposition and highly reversible Zn plating/stripping behaviors were realized. Besides, improved electrochemical performances can also be achieved in Zn//MnO2 full cells by taking the advantages of this triple-functional additive. This work provides a new strategy for stabilizing Zn anodes from a comprehensive perspective

Deep interest shifting network with meta embeddings for fresh item recommendation

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications

RIS-based IMT-2030 Testbed for MmWave Multi-stream Ultra-massive MIMO Communications

As one enabling technique of the future sixth generation (6G) network, ultra-massive multiple-input-multiple-output (MIMO) can support high-speed data transmissions and cell coverage extension. However, it is hard to realize the ultra-massive MIMO via traditional phased arrays due to unacceptable power consumption. To address this issue, reconfigurable intelligent surface-based (RIS-based) antennas are an energy-efficient enabler of the ultra-massive MIMO, since they are free of energy-hungry phase shifters. In this article, we report the performances of the RIS-enabled ultra-massive MIMO via a project called Verification of MmWave Multi-stream Transmissions Enabled by RIS-based Ultra-massive MIMO for 6G (V4M), which was proposed to promote the evolution towards IMT-2030. In the V4M project, we manufacture RIS-based antennas with 1024 one-bit elements working at 26 GHz, based on which an mmWave dual-stream ultra-massive MIMO prototype is implemented for the first time. To approach practical settings, the Tx and Rx of the prototype are implemented by one commercial new radio base station and one off-the-shelf user equipment, respectively. The measured data rate of the dual-stream prototype approaches the theoretical peak rate. Our contributions to the V4M project are also discussed by presenting technological challenges and corresponding solutions.Comment: 8 pages, 5 figures, to be published in IEEE Wireless Communication

Three-Dimensional Manganese Oxide@Carbon Networks as Free-Standing, High-Loading Cathodes for High-Performance Zinc-Ion Batteries

Zinc-ion batteries (ZIBs), which are inexpensive and environmentally friendly, have a lot of potential for use in grid-scale energy storage systems, but their use is constrained by the availability of suitable cathode materials. MnO2-based cathodes are emerging as a promising contenders, due to the great availability and safety, as well as the device's stable output voltage platform (1.5 V). Improving the slow kinetics of MnO2-based cathodes caused by low electrical conductivity and mass diffusion rate is a challenge for their future use in next-generation rapid charging devices. Herein, the aforementioned challenges are overcome by proposing a sodium-intercalated manganese oxide (NMO) with 3D varying thinness carbon nanotubes (VTCNTs) networks as appropriate free-standing, binder-free cathodes (NMO/VTCNTs) without any heat treatment. A network construction strategy based on CNTs of different diameters is proposed for the first time to provide high specific capacity while achieving high mass loading. The specific capacity of as-prepared cathodes is significantly increased. The resulting free-standing binder-free cathodes achieve excellent capacity (329 mAh g−1 after 120 cycles at 0.2 A g−1 and 225 mAh g−1 after 200 cycles at 1 A g−1) and long-term cycling stability (158 mAh g−1 at 2 A g−1 after 1000 cycles)

Three-Dimensional Manganese Oxide@Carbon Networks as Free-Standing, High-Loading Cathodes for High-Performance Zinc-Ion Batteries

Zinc-ion batteries (ZIBs), which are inexpensive and environmentally friendly, have a lot of potential for use in grid-scale energy storage systems, but their use is constrained by the availability of suitable cathode materials. MnO2-based cathodes are emerging as a promising contenders, due to the great availability and safety, as well as the device's stable output voltage platform (1.5 V). Improving the slow kinetics of MnO2-based cathodes caused by low electrical conductivity and mass diffusion rate is a challenge for their future use in next-generation rapid charging devices. Herein, the aforementioned challenges are overcome by proposing a sodium-intercalated manganese oxide (NMO) with 3D varying thinness carbon nanotubes (VTCNTs) networks as appropriate free-standing, binder-free cathodes (NMO/VTCNTs) without any heat treatment. A network construction strategy based on CNTs of different diameters is proposed for the first time to provide high specific capacity while achieving high mass loading. The specific capacity of as-prepared cathodes is significantly increased. The resulting free-standing binder-free cathodes achieve excellent capacity (329 mAh g−1 after 120 cycles at 0.2 A g−1 and 225 mAh g−1 after 200 cycles at 1 A g−1) and long-term cycling stability (158 mAh g−1 at 2 A g−1 after 1000 cycles)