Discrete Factorization Machines for Fast Feature-based Recommendation

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201

On the performance of a mixed RF/MIMO FSO variable gain dual-hop transmission system

In this work, we propose a mixed radio frequency (RF) and multiple-input-multiple-output (MIMO) free-space optical (FSO) system based on a variable-gain dual-hop relay transmission scheme. The RF channel is modeled by Rayleigh distribution and Gamma–Gamma turbulence distribution is adopted for the MIMO FSO link, which accounts for the equal gain combining diversity technique. Moreover, new closed-form mathematical formulas are obtained including the cumulative distribution function, probability density function, moment generating function, and moments of equivalent signal-to-noise ratio of the dual-hop relay system based on Meijer’s G function. As such, we derive the novel analytical expressions of the outage probability, the higher-order fading, and the average bit error rate for a range of modulations in terms of Meijer’s G function. Furthermore, the exact closed-form formula of the ergodic capacity is derived based on the bivariate Meijer’s G function. The evaluation and simulation are provided for system performance, and the effect of spatial diversity technique is discussed as well

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor

Unsupervised Temporal Action Localization via Self-paced Incremental Learning

Recently, temporal action localization (TAL) has garnered significant interest in information retrieval community. However, existing supervised/weakly supervised methods are heavily dependent on extensive labeled temporal boundaries and action categories, which is labor-intensive and time-consuming. Although some unsupervised methods have utilized the ``iteratively clustering and localization'' paradigm for TAL, they still suffer from two pivotal impediments: 1) unsatisfactory video clustering confidence, and 2) unreliable video pseudolabels for model training. To address these limitations, we present a novel self-paced incremental learning model to enhance clustering and localization training simultaneously, thereby facilitating more effective unsupervised TAL. Concretely, we improve the clustering confidence through exploring the contextual feature-robust visual information. Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance. Extensive experiments on two public datasets have substantiated the superiority of our model over several state-of-the-art competitors

Associations of trajectories in body roundness index with incident cardiovascular disease: a prospective cohort study in rural China

AimsThe body roundness index (BRI) has good predictive ability for both body fat and visceral adipose tissue. Longitudinal BRI trajectories can reveal the potential dynamic patterns of change over time. This prospective study assessed potential associations between BRI trajectories and incident cardiovascular disease (CVD) in rural regions of Northeast China.MethodsIn total, 13,209 participants (mean age: 49.0 ± 10.3 years, 6,856 [51.9%] male) were enrolled with three repeated times of BRI measurements at baseline (2004–2006), 2008, and 2010, and followed up until 2017 in this prospective study. Using latent mixture model, the BRI trajectories were determined based on the data from baseline, 2008 and 2010. Composite CVD events (myocardial infarction, stroke, and CVD death combined) was the primary endpoint. Cox proportional-hazards models were used to analyze the longitudinal associations between BRI trajectories and incident CVD.ResultsThree distinct BRI trajectories were identified: high-stable (n = 538), moderate-stable (n = 1,542), and low-stable (n = 11,129). In total, 1,382 CVD events were recorded during follow-up. After adjustment for confounders, the moderate-stable and high-stable BRI groups had a higher CVD risk than did the low-stable BRI group, and the HR (95%CI) were 1.346 (1.154, 1.571) and 1.751 (1.398, 2.194), respectively. Similar associations were observed between the trajectories of BRI and the risk of stroke and CVD death. The high-stable group was also significantly and independently associated with CVD, myocardial infarction, stroke, and CVD death in participants aged <50 years.ConclusionBRI trajectory was positively associated with incident CVD, providing a novel possibility for the primary prevention of CVD in rural regions of China

On the design evolution of hip implants: A review

This manuscript reviews the development of femoral stem prostheses in the biomedical field. After a brief introduction on the development of these prostheses and the associated problems, we describe the standard design of these systems. We review the different materials, constructions, and surfaces used in the development of femoral stems, in order to solve and avoid various problems associated with their use. Femoral stem prostheses have undergone substantial changes and design optimizations since their introduction. Common materials include stainless steel, cobalt–chromium alloy, titanium alloy, and composites. The structural development of femoral stem prostheses, including their length, shape, porosity, and functional gradient construction, is also reviewed. The performance of these prostheses is affected not only by individual factors, but also by the synergistic combination of multiple effects; therefore, several aspects need to be optimized. The main purpose of this study is to summarize various strategies for the material and construction optimization of femoral stem prostheses, and to provide a reference for the combined optimization of their performance. Substantial research is still needed to develop prostheses emulating the behavior of a real human femoral stem

Fast Ionic Diffusion-Enabled Nanoflake Electrode by Spontaneous Electrochemical Pre-Intercalation for High-Performance Supercapacitor

Layered intercalation compounds Na$_{x}$MnO$_{2}$ (x = 0.7 and 0.91) nanoflakes have been prepared directly through wet electrochemical process with Na$^{+}$ ions intercalated into MnO$_{2}$ interlayers spontaneously. The as-prepared Na$_{x}$MnO$_{2}$ nanoflake based supercapacitors exhibit faster ionic diffusion with enhanced redox peaks, tenfold-higher energy densities up to 110 Wh·kg$^{-1}$ and higher capacitances over 1000 F·g$^{-1}$ in aqueous sodium system compared with traditional MnO$_{2}$ supercapacitors. Due to the free-standing electrode structure and suitable crystal structure, Na$_{x}$MnO$_{2}$ nanoflake electrodes also maintain outstanding electrochemical stability with capacitance retention up to 99.9% after 1000 cycles. Besides, pre-intercalation effect is further studied to explain this enhanced electrochemical performance. This study indicates that the suitable pre-intercalation is effective to improve the diffusion of electrolyte cations and other electrochemical performance for layered oxides, and suggests that the as-obtained nanoflakes are promising materials to achieve the hybridization of both high energy and power density for advanced supercapacitors.Chemistry and Chemical Biolog