Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Traffic Management for Mobile Cloud Services in 5G Networks

Mobile cloud computing has been introduced to improve the performance of mobile application clients by offloading data processing and storage to cloud. By deploying the service on several cloud-enabled data centers, the service provider can optimally locate service instances on the cloud to provide qualified services at a reasonable cost. However, a centralized approach for both request allocation and response routing does not scale efficiently due to a large number of mobile clients involved in the mobile service traffic management. Moreover, the random and unpredictable wireless network performance (e.g., delay) complicates the problem further. In this paper, we present a stochastic distributed optimization framework for mobile cloud traffic management in 5G networks. The framework takes the impact of random wireless network characteristics into account. Utilizing the alternating direction method of multipliers, the optimization problem is decomposed into independent subproblems, which are solved in a parallel fashion on distributed agents and coordinated through dual variables. The convergence issue under the stochastic setting is addressed, and the numerical tests validate the effectiveness of the proposed algorithm.
Context Awareness Group Buying in D2D Networks: A Coalition Formation Game-Theoretic Approach

In this paper, we proposed a context-aware group buying mechanism to reduce users’ data cost based on the content similarity. Each user's cost is formulated as the combination of the content-aware data cost and location-aware sharing cost. Data cost is the payoff of the spectrum owner's channel to download files and sharing cost is the energy and time cost in transmitting files among the coalition. Compared with downloading data alone, users would like to form different groups and download the traffic data first and then share data among the group to achieve a lower cost. The cost reducing problem through group buying mechanism is modeled as a coalition formation game (CFG). Besides the traditional Pareto order, a coalition order maximizing the coalition's benefit and a selfish order maximizing users’ benefit are proposed. The CFGs with the two proposed orders are proved to be potential games, respectively, and the existence of the stable coalition partitions are also guaranteed by Nash equilibria. A cooperative exchange mechanism is designed, where users can make decisions cooperatively to achieve better performance. Simulation results show that the context awareness group buying reduces the cost and improves the benefit significantly compared with the situation without context awareness. The proposed orders both have better performance than the Pareto order.
Joint Head Attribute Classifier and Domain-Specific Refinement Networks for Face Alignment

In this article, a two-stage refinement network is proposed for facial landmarks detection on unconstrained conditions. Our model can be divided into two modules, namely the Head Attribude Classifier (HAC) module and the Domain-Specific Refinement (DSR) module. Given an input facial image, HAC adopts multi-task learning mechanism to detect the head pose and obtain an initial shape. Based on the obtained head pose, DSR designs three different CNN-based refinement networks trained by specific domain, respectively, and automatically selects the most approximate network for the landmarks refinement. Different from existing two-stage models, HAC combines head pose prediction with facial landmarks estimation to improve the accuracy of head pose prediction, as well as obtaining a robust initial shape. Moreover, an adaptive sub-network training strategy applied in the DSR module can effectively solve the issue of traditional multi-view methods that an improperly selected sub-network may result in alignment failure. The extensive experimental results on two public datasets, AFLW and 300W, confirm the validity of our model.
Augmenting max-weight with explicit learning for wireless scheduling with switching costs

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to opportunistically switch off a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach — a Max-Weight algorithm along with a Lyapunov-based stability argument — does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.
GLP: A Novel Framework for Group-Level Location Promotion in Geo-Social Networks

Location-aware viral marketing is crucial in modern commercial applications for attracting customers to certain points of interests. Prior works are mainly based on formulating it into a location-aware influence maximization problem in Geo-social Networks (GSNs), where $K$ initial seed individuals are selected in hope of maximizing the number of final influenced users. In this paper, we present the first look into the group-level location promotion, which can potentially enhance its performance, with the phenomenon that users belonging to the same geo-community share similar moving preferences. We propose GLP, a new and novel framework of group-level location promotion by virtue of geo-communities, each of which is treated as a group in GSNs. Aiming to attract more users to designated locations, GLP firstly carries out user grouping through an iterative learning approach based on information extraction from massive check-ins records. The advantage of GLP is three-folded: i) by aggregating movements of group members, GLP significantly avoids the sparsity and sporadicity of individual check-ins, and thus obtains more reliable mobility models; ii) by generalizing a new group-level social graph, GLP can exponentially reduce the computational complexity of seed nodes selection that is algorithmically executed by a greedy algorithm; iii) in comparison with prior individual-level cases, GLP is theoretically demonstrated to drastically increase influence spread under the same given budget. Extensive experiments on real datasets demonstrate that the GLP outperforms four baselines, with notably up to 10 times larger influence spread and 100 times faster seed selection over two individual-level cases, meanwhile verifying the impact of group numbers in final influence spread.
Computationally Efficient Constant-Envelope Precoding Algorithm for MISO Multicasting Using PSK Modulation

Constant envelope (CE) precoding is a transmission scheme facilitating the use of low-cost radio-frequency power amplifiers in the transmitter. Herein, we consider CE precoder designs for multiple-input–single-output multicast systems using phase-shift-keying modulation. A new CE precoder is proposed that first smooths the objective function in the max–min received signal-to-noise-ratio (RSNR) design problem, which is then solved through the projected gradient framework. Simulation results revealed that the proposed CE precoders can achieve similar or superior minimum RSNR with much lower computational complexity compared with existing semidefinite-relaxation-based CE precoders.
Subcarrier and Power Allocation for the Downlink of Multicarrier NOMA Systems

This paper investigates the joint subcarrier and power allocation problem for the downlink of a multi-carrier non-orthogonal multiple access (MC-NOMA) system. A novel three-step resource allocation framework is designed to deal with the sum rate maximization problem. In Step 1, we relax the problem by assuming that each of the users can use all the subcarriers simultaneously. With this assumption, we prove the convexity of the resultant power control problem and solve it via convex programming tools to get a power vector for each user. In Step 2, we allocate the subcarriers to users by a heuristic greedy manner with the obtained power vectors in Step 1. In Step 3, the proposed power control schemes used in Step 1 are applied once more to further improve the system performance with the obtained subcarrier assignment of Step 2. To solve the maximization problem with fixed subcarrier assignments in both Step 1 and Step 3, a centralized power allocation method based on projected gradient descent algorithm and two distributed power control strategies based, respectively, on pseudo-gradient algorithm and iterative waterfilling algorithm are investigated. Numerical results show that our proposed three-step resource allocation algorithm could achieve comparable sum rate performance to the existing near-optimal solution with much lower computational complexity and outperforms power controlled OMA scheme. Besides, a tradeoff between user fairness and sum rate performance can be achieved via applying different user power constraint strategies in the proposed algorithm.
Block Pilot Based Channel Estimation and High- Accuracy Signal Detection for GSM-OFDM Systems on High-Speed Railways

In this paper, generalized spatial modulation-orthogonal frequency-division multiplexing is introduced to wireless communication system on high-speed railways for the first time. There are two main challenges to be tackled. On the one hand, channel estimation is difficult as small number of subcarriers are activated; on the other hand, as both the dimensions of the channel matrix and the number of nonzero elements of the unknown signals are large, the matching based and the compressive sensing based signal detectors may not work effectively. To overcome these problems, we first propose a new channel estimation scheme, in which the block pilot pattern instead of the comb pilot pattern is used and a novel interpolation method, which takes the time variation property in different symbols into consideration is adopted. Simulation results demonstrate that in comparison to the conventional interpolation methods, our proposed method achieves better normalized mean square error performance. Then, for the signal detection, we adopt the decomposition and iteration to reduce the dimension of the matrix to be processed. Based on the decomposed structure, the maximum likelihood method is conducted in the solution space to detect the signal. Simulation results demonstrate that the proposed detector presents higher accuracy compared with the existing signal detection schemes.
Modeling of Ladder-Secondary-Linear Induction Machine Using Magnetic Equivalent Circuit

The paper presents a novel technique for modeling ladder-secondary single-sided linear induction machines. Saturation effect is considered by core non-linearity characteristic for both primary and secondary. The proposed modeling method is based on magnetic equivalent circuit with adjustable accuracy. The machine parameters such as its dimensions, pole numbers, bar numbers, and also model accuracy can be chosen arbitrary in the proposed model. The end effect is considered by virtual teeth at the entry and exit ends of the machine primary. The results of the proposed method are compared with finite element method (FEM) in terms of accuracy and processing time. The results show the effectiveness of the proposed method. In addition, due to having long processing time of the FEM, the proposed method is a very suitable manner for modeling of this particular machine. Also, the accuracy of the proposed method has been confirmed by experimental results, in starting.
ResFusion: deeply fused scene parsing network for RGB-D images

Scene parsing is a very challenging work for complex and diverse scenes. In this study, the authors address the problem of semantic segmentation of indoor scenes for red, green, blue-depth (RGB-D) images. Most existing works use only the colour or photometric information for this problem. Here, they present an approach to fusing feature maps between colour network branch and depth network branch to integrate the photometric information and geometric information, which improves the semantic segmentation performance. They propose a novel convolutional neural network that uses ResNet as a baseline network. Their proposed network adopts a spatial pyramid pooling module to make full use of different sub-region representations. Their proposed network also adopts multiple feature maps fusion modules to integrate texture and structure information between the colour branch and depth branch. Moreover, their proposed network has multiple auxiliary loss branches together with the main loss function to prevent the gradient of frontal layers disappear and accelerate the training phase of the fusion part. Comprehensive experimental evaluations show that their proposed network `ResFusion' improves the performance greatly over the baseline network and has achieved competitive performance compared with other state-of-the-art methods on the challenging SUN RGB-D benchmark.
A Distributed Approach to Improving Spectral Efficiency in Uplink Device-to-Device-Enabled Cloud Radio Access Networks

Device-to-device (D2D)-enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two users. However, due to the need to acquire global channel state information (CSI) and to execute centralized algorithms, heavy burdens are placed on the fronthaul and the baseband unit (BBU) pool. To alleviate these burdens, a distributed approach to mode selection and resource allocation for potential D2D pairs under pre-determined resource allocation of C-RAN users is proposed, in which pairs of users are endowed with decision-making capabilities. The proposed procedure is divided into three stages: communication mode and subchannel selection, utility value determination, and reinforcement-learning-based strategy update. The core idea is that the D2D pairs self-optimize the mode selection and resource allocation without global CSI under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the impacts of the fronthaul capacity, the centralized signal processing capability of the BBU pool, and the distance between the D2D transmitter and the remote radio head are demonstrated and analyzed.
PRObE: A Thousand-Node Experimental Cluster for Computer Systems Research

If you have ever aspired to create a software system that can harness a thousand computers and perform some impressive feat, you know the dismal prospects of finding such a cluster ready and waiting for you to make magic with it. Today, however, if you are a systems researcher and your promised feat is impressive enough, there is such a resource available online: PRObE. This article is an introduction to and call for proposals for use of the PRObE facilities.
A Review of Estimation for Vehicle Tire-Road Interactions Toward Automated Driving

This paper proposes an extensive overview of the tire-road interaction estimation issue as it relates to automated driving from the prospectives of sensor configuration, tire modeling, and estimation approaches. The tire-road interactions needed for estimation are first determined and classified. Then, the sensor configuration schemes of different types of tire-road interactions are presented and analyzed. The following introduces various types of tire models and provides the limitations and advantages of different estimation approaches based on categorizing and summarizing those techniques. Moreover, some interesting perspectives for future research are listed based on the extensive experience of the authors.
The Influencing Factors of Serum Lipids among Middle-aged Women in Northeast China

Background Dyslipidemia is a common and serious health problem, especially in middle-aged women. We aimed to reveal quantile-specific associations of serum lipids [triglycerides (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-c) and high density lipoprotein cholesterol (HDL-c)] with influencing factors in middle-aged women. Methods A sample of 5635 participants were enrolled from Jilin, China, in 2012. Quantile regression (QR) model was performed to identify factors which influenced serum lipids in different quantiles. Results The influencing factors of TG, TC, LDL-c and HDL-c were different. Waist circumference (WC), menopause, smoking, diabetes and hypertension were positively associated with TG in almost all quantiles; Menopause and age were positively associated with TC in almost all quantiles. WC, living in urban areas and alcohol consumption were positively associated with TC in low and middle quantiles, diabetes was positively associated with TC from P50 to P95. The result of LDL-c was similar to TC; BMI was negatively associated with HDL-c from P50 to P90. WC and diabetes were negatively associated with HDL-c from P5 to P90. Conclusion Among middle-aged women, menopause, diabetes and WC were the main factors affecting the serum lipids. Postmenopausal women would get more risk in increasing the level of serum lipids.
Is obstructive sleep apnea the missing link between metabolic syndrome and second-generation antipsychotics: Preliminary study

Background: Metabolic syndrome in individuals taking second-generation antipsychotics is thought to be mediated by antipsychotic-induced weight gain. However, recent literature challenges this notion, and theoretically, it may also be mediated through obstructive sleep apnea (OSA). This study explores the contribution of OSA in antipsychotic-induced metabolic syndrome. Materials and Methods: Forty-three participants suffering from schizophrenia spectrum disorder and major depressive disorder, taking second-generation antipsychotics were included in this study. Treatment history was taken in detail, and lifetime exposure to antipsychotics was converted to olanzapine-equivalent doses. Physical characteristics were noted. OSA was screened through the Hindi version of Berlin Questionnaire. Plasma glucose, serum total cholesterol, serum high-density lipoprotein, and serum triglyceride were measured after 12-h fasting. Adult treatment Panel-III criteria were used to diagnose metabolic syndrome. Results: Gender distribution was comparable in the study sample. About 27% had continuous illness, 25.6% of participants had metabolic syndrome, and 20.9% were at high risk for sleep apnea. Participants with and without metabolic syndrome were comparable with regard to demographic variables, duration of illness, and lifetime exposure to antipsychotics. Logistic regression depicted that OSA (odds ratio [OR] = 15.09), waist circumference (OR = 1.15), and fasting plasma glucose (OR = 1.21) increased the risk of metabolic syndrome. Conclusion: Results of the present study suggest that metabolic syndrome in participants taking second-generation antipsychotics is mediated through OSA.
Detection of bacterial endosymbionts in freshwater crustaceans: the applicability of non-degenerate primers to amplify the bacterial 16S rRNA gene

Bacterial endosymbionts of aquatic invertebrates remain poorly studied. This is at least partly due to a lack of suitable techniques and primers for their identification. We designed a pair of non-degenerate primers which enabled us to amplify a fragment of ca. 500 bp of the 16S rRNA gene from various known bacterial endosymbiont species. By using this approach, we identified four bacterial endosymbionts, two endoparasites and one uncultured bacterium in seven, taxonomically diverse, freshwater crustacean hosts from temporary waters across a wide geographical area. The overall efficiency of our new WOLBSL and WOLBSR primers for amplification of the bacterial 16S rRNA gene was 100%. However, if different bacterial species from one sample were amplified simultaneously, sequences were illegible, despite a good quality of PCR products. Therefore, we suggest using our primers at the first stage of bacterial endosymbiont identification. Subsequently, genus specific primers are recommended. Overall, in the era of next-generation sequencing our method can be used as a first simple and low-cost approach to identify potential microbial symbionts associated with freshwater crustaceans using simple Sanger sequencing. The potential to detected bacterial symbionts in various invertebrate hosts in such a way will facilitate studies on host-symbiont interactions and coevolution.
Emerging targeted strategies for the treatment of autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is a widespread genetic disease that leads to renal failure in the majority of patients. The very first pharmacological treatment, tolvaptan, received Food and Drug Administration approval in 2018 after previous approval in Europe and other countries. However, tolvaptan is moderately effective and may negatively impact a patient's quality of life due to potentially significant side effects. Additional and improved therapies are still urgently needed, and several clinical trials are underway, which are discussed in the companion paper Müller and Benzing (Management of autosomal-dominant polycystic kidney disease-state-of-the-art) Clin Kidney J 2018; 11: i2-i13. Here, we discuss new therapeutic avenues that are currently being investigated at the preclinical stage. We focus on mammalian target of rapamycin and dual kinase inhibitors, compounds that target inflammation and histone deacetylases, RNA-targeted therapeutic strategies, glucosylceramide synthase inhibitors, compounds that affect the metabolism of renal cysts and dietary restriction. We discuss tissue targeting to renal cysts of small molecules via the folate receptor, and of monoclonal antibodies via the polymeric immunoglobulin receptor. A general problem with potential pharmacological approaches is that the many molecular targets that have been implicated in ADPKD are all widely expressed and carry out important functions in many organs and tissues. Because ADPKD is a slowly progressing, chronic disease, it is likely that any therapy will have to continue over years and decades. Therefore, systemically distributed drugs are likely to lead to potentially prohibitive extra-renal side effects during extended treatment. Tissue targeting to renal cysts of such drugs is one potential way around this problem. The use of dietary, instead of pharmacological, interventions is another.
In Situ Activating Strategy to Significantly Boost Oxygen Electrocatalysis of Commercial Carbon Cloth for Flexible and Rechargeable Zn‐Air Batteries

An in situ strategy to simultaneously boost oxygen reduction and oxygen evolution (ORR/OER) activities of commercial carbon textiles is reported and the direct use of such ubiquitous raw material as low‐cost, efficient, robust, self‐supporting, and bifunctional air electrodes in rechargeable Zn‐air batteries is demonstrated. This strategy not only furnishes carbon textiles with a large surface area and hierarchical meso‐microporosity, but also enables efficient dual‐doping of N and S into carbon skeletons while retaining high conductivity and stable monolithic structures. Thus, although original carbon textile has rather poor catalytic activity, the activated textiles without loading other active materials yield effective ORR/OER bifunctionality and stability with a much lower reversible overpotential (0.87 V) than those of Pt/C (1.10 V) and RuO2 (1.02 V) and many reported metal‐free bifunctional catalysts. Importantly, they can concurrently function as current collectors and as ORR/OER catalysts for rechargeable aqueous and flexible solid‐state Zn‐air batteries, showing excellent cell performance, long lifetime, and high flexibility.