D2D-Assisted Mobile Edge Computing: Optimal Scheduling under Uncertain Processing Cycles and Intermittent Communications

Mobile edge computing (MEC) has been regarded as a promising approach to deal with explosive computation requirements by enabling cloud computing capabilities at the edge of networks. Existing models of MEC impose some strong assumptions on the known processing cycles and unintermittent communications. However, practical MEC systems are constrained by various uncertainties and intermittent communications, rendering these assumptions impractical. In view of this, we investigate how to schedule task offloading in MEC systems with uncertainties. First, we derive a closed-form expression of the average offloading success probability in a device-to-device (D2D) assisted MEC system with uncertain computation processing cycles and intermittent communications. Then, we formulate a task offloading maximization problem (TOMP), and prove that the problem is NP-hard. For problem solving, if the problem instance exhibits a symmetric structure, we propose a task scheduling algorithm based on dynamic programming (TSDP). By solving this problem instance, we derive a bound to benchmark sub-optimal algorithm. For general scenarios, by reformulating the problem, we propose a repeated matching algorithm (RMA). Finally, in performance evaluations, we validate the accuracy of the closed-form expression of the average offloading success probability by Monte Carlo simulations, as well as the effectiveness of the proposed algorithms

Robust Divergence Angle for Inter-satellite Laser Communications under Target Deviation Uncertainty

Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. One solution to mitigate the issue is to use a divergence angle at the transmitter being wider than that if the receiver position is perfectly known. However, as how the deviation would vary over time is generally very hard to predict or model, there is no clear clue for setting the divergence angle. We propose a robust optimization approach to the problem, with the advantage that no distribution of the deviation need to be modelled. Instead, a so-called uncertainty set (often defined in form of a convex set such as a polytope) is used, where each element represents a possible scenario, i.e., a sequence of deviation values over time. Robust optimization seeks the solution that maximizes the performance (e.g., sum rate) that can be guaranteed, no matter which scenario in the uncertainty set materializes. To solve the robust optimization problem, we deploy a process of alternately solving a decision maker's problem and an adversarial problem. The former optimizes the divergence angle for a subset of the uncertainty set, whereas the latter is used to explore if the subset needs to be augmented. Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes

Optical neural network architecture for deep learning with the temporal synthetic dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems. This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions, which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks

Joint Effect of Genotypic and Phenotypic Features of Reproductive Factors on Endometrial Cancer Risk

Prolonged estrogen exposure is believed to be the major cause of endometrial cancer. As possible markers of estrogen exposure, various menstrual and reproductive features, e.g., ages at menarche and menopause, are found to be associated with endometrial cancer risk. In order to assess their combined effects on endometrial cancer, we created the total number of menstrual cycles (TNMC) that a woman experienced during her life or up to the time of study and two genetic risk scores, GRS1 for age at menarche and GRS2 for age at menopause. Comparing 482 endometrial cancer patients with 571 population controls, we found TNMC was associated with endometrial cancer risk and that the association remained statistically significant after adjustment for obesity and other potential confounders. Risk increased by about 2.5% for every additional 10 menstrual-cycles. The study also showed that high GRS1 was associated with increased risk. This relationship, however, was attenuated after adjustment for obesity. Our study further indicated women with high TNMC and GRS1 had twice the risk of endometrial cancer compared to those low in both indices. Our results provided additional support to the involvement of estrogen exposure in endometrial cancer risk with regard to genetic background and lifestyle features

Genetic susceptibility to hepatocellular carcinoma in chromosome 22q13.31, findings of a genome-wide association study.

Background and Aim: Chronic hepatitis C virus (HCV) infection, long-term alcohol use, cigarette smoking, and obesity are the major risk factors for hepatocellular carcinoma (HCC) in the United States, but the disease risk varies substantially among individuals with these factors, suggesting host susceptibility to and gene-environment interactions in HCC. To address genetic susceptibility to HCC, we conducted a genome-wide association study (GWAS). Methods: Two case-control studies on HCC were conducted in the United States. DNA samples were genotyped using the Illumian microarray chip with over 710 000 single nucleotide polymorphisms (SNPs). We compared these SNPs between 705 HCC cases and 1455 population controls for their associations with HCC and verified our findings in additional studies. Results: In this GWAS, we found that two SNPs were associated with HCC at Conclusions: SNPs i

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Resource Optimization With Interference Coupling in Multi-RIS-Assisted Multi-Cell Systems

Deploying reconfigurable intelligent surface (RIS) to enhance wireless transmission is a promising approach. In this paper, we investigate large-scale multi-RIS-assisted multi-cell systems, where multiple RISs are deployed in each cell. Different from the full-buffer scenario, the mutual interference in our system is not known a priori, and for this reason we apply the load coupling model to analyze this system. The objective is to minimize the total resource consumption subject to user demand requirement by optimizing the reflection coefficients in the cells. The cells are highly coupled and the overall problem is non-convex. To tackle this, we first investigate the single-cell case with given interference, and propose a low-complexity algorithm based on the Majorization-Minimization method to obtain a locally optimal solution. Then, we embed this algorithm into an algorithmic framework for the overall multi-cell problem, and prove its feasibility and convergence to a solution that is at least locally optimal. Simulation results demonstrate the benefit of RIS in time-frequency resource utilization in the multi-cell system

Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

In this paper, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches