On the convergence rate of distributed gradient methods for finite-sum optimization under communication delays

Motivated by applications in machine learning and statistics, we study distributed optimization problems over a network of processors, where the goal is to optimize a global objective composed of a sum of local functions. In these problems, due to the large scale of the data sets, the data and computation must be distributed over processors resulting in the need for distributed algorithms. In this paper, we consider a popular distributed gradient-based consensus algorithm, which only requires local computation and communication. An important problem in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems. We prove the convergence of the gradient-based consensus algorithm in the presence of uniform, but possibly arbitrarily large, communication delays between the processors. Moreover, we obtain an upper bound on the rate of convergence of the algorithm as a function of the network size, topology, and the inter-processor communication delays

Predicting success for new flavors with information known pre-launch: a flavored snack food case study

Success in the marketplace is the goal of every product launch. Knowing what data to collect before launching a product that could predict success would be valuable to companies. Thus, the objective of this study was to determine whether success of new line extensions for a multi-flavored snack product available internationally could be predicted from information available before launch. Staff from 15 countries completed a questionnaire for each product and included questions related to authenticity, familiarity, and capturing current trends, packaging and market place issues such as product competition and pricing. Using 63 flavors, a discriminant function correctly identified 75.8% successful products as successful and 66.7% unsuccessful products as unsuccessful. Stepwise comparison determined the variables necessary to correctly categorize the snack products: being a trendy flavor, new to the category, based off foods from restaurants or traditional foods. These variables assisted in predicting in market success for this product category

Metagenomic deep sequencing of aqueous fluid detects intraocular lymphomas.

IntroductionCurrently, the detection of pathogens or mutations associated with intraocular lymphomas heavily relies on prespecified, directed PCRs. With metagenomic deep sequencing (MDS), an unbiased high-throughput sequencing approach, all pathogens as well as all mutations present in the host's genome can be detected in the same small amount of ocular fluid.MethodsIn this cross-sectional case series, aqueous fluid samples from two patients were submitted to MDS to identify pathogens as well as common and rare cancer mutations.ResultsMDS of aqueous fluid from the first patient with vitreal lymphoma revealed the presence of both Epstein-Barr virus (HHV-4/EBV) and human herpes virus 8 (HHV-8) RNA. Aqueous fluid from the second patient with intraocular B-cell lymphoma demonstrated a less common mutation in the MYD88 gene associated with B-cell lymphoma.ConclusionMDS detects pathogens that, in some instances, may drive the development of intraocular lymphomas. Moreover, MDS is able to identify both common and rare mutations associated with lymphomas

Achieving sub-diffraction imaging through bound surface states in negative-refracting photonic crystals at the near-infrared

We report the observation of imaging beyond the diffraction limit due to bound surface states in negative refraction photonic crystals. We achieve an effective negative index figure-of-merit [-Re(n)/Im(n)] of at least 380, ~125x improvement over recent efforts in the near-infrared, with a 0.4 THz bandwidth. Supported by numerical and theoretical analyses, the observed near-field resolution is 0.47 lambda, clearly smaller than the diffraction limit of 0.61 lambda. Importantly, we show this sub-diffraction imaging is due to the resonant excitation of surface slab modes, allowing refocusing of non-propagating evanescent waves

Analisis Throughput Dan Skalabilitas Virtualized Network Function VyOS Pada Hypervisor VMWare ESXi, XEN, DAN KVM

Virtualisasi berjalan diatas suatu hypervisor yang merupakan suatu program untuk membuat dan menjalankan virtual machine. Hypervisor mengatur sejumlah resources hardware seperti RAM, CPU, dan storage yang dimiliki hardware aslinya untuk digunakan bersama-sama dengan virtual environment. Salah satu implementasi yang dapat dimanfaatkan dengan adanya virtualisasi adalah Network Function Virtualization atau yang biasa disebut NFV. Konsep ini memanfaatkan teknik virtualisasi untuk membuat suatu Virtualized Network Function (VNF) yang memiliki fungsi sama dengan network device aslinya. Salah satu VNF yang dapat digunakan secara bebas adalah VyOS. VyOS merupakan sistem operasi jaringan berbasis open source yang memiliki fungsi seperti hardware router tradicional, firewall. VPN, proxy dan fungsi jaringan lainnya. Pada penelitian ini dilakukan pengujian performansi VyOS pada bare-metal hypervisor (XEN, VMware ESXi) dan hosted hypervisor (Kernel-based Virtual Machine atau KVM).  Penelitian ini bertujuan untuk mengetahui kinerja dari ketiga hypervisor tersebut dalam menjalankan VNF dengan parameter throughput, dan parameter skalabilitas. Dari hasil pengujian dan análisis dapat disimpulkan bahwa KVM memiliki performansi kecepatan tertinggi dengan besar throughput 19.29 GB/s. Sedangkan untuk parameter skalabilitas, VMware memiliki skalabilitas yang sangat baik yang ditunjukkan dengan kecilnya degradasi performansi pada throughput saat menjalankan banyak VNF daripada XEN dan KVM

Autocorrelation analysis for the unbiased determination of power-law exponents in single-quantum-dot blinking

We present an unbiased and robust analysis method for power-law blinking statistics in the photoluminescence of single nano-emitters, allowing us to extract both the bright- and dark-state power-law exponents from the emitters' intensity autocorrelation functions. As opposed to the widely-used threshold method, our technique therefore does not require discriminating the emission levels of bright and dark states in the experimental intensity timetraces. We rely on the simultaneous recording of 450 emission timetraces of single CdSe/CdS core/shell quantum dots at a frame rate of 250 Hz with single photon sensitivity. Under these conditions, our approach can determine ON and OFF power-law exponents with a precision of 3% from a comparison to numerical simulations, even for shot-noise-dominated emission signals with an average intensity below 1 photon per frame and per quantum dot. These capabilities pave the way for the unbiased, threshold-free determination of blinking power-law exponents at the micro-second timescale

Theory for the electromigration wind force in dilute alloys

A multiple scattering formulation for the electromigration wind force on atoms in dilute alloys is developed. The theory describes electromigration via a vacancy mechanism. The method is used to calculate the wind valence for electromigration in various host metals having a close-packed lattice structure, namely aluminum, the noble metals copper, silver and gold and the $4d$ transition metals. The self-electromigration results for aluminum and the noble metals compare well with experimental data. For the $4d$ metals small wind valences are found, which make these metals attractive candidates for the experimental study of the direct valence.Comment: 18 pages LaTeX, epsfig, 8 figures. to appear in Phys. Rev. B 56 of 15/11/199

Impact of irreversibility and uncertainty on the timing of infrastructure projects

This paper argues that because of the irreversibility and uncertainty associated with Build - Operate - Transfer (BOT) infrastructure projects, their financial evaluation should also routinely include the determination of the value of the option to defer the construction start-up. This ensures that project viability is comprehensively assessed before any revenue or loan guarantees are considered by project sponsors to support the project. This paper shows that the framework can be used even in the context of the intuitive binomial lattice model. This requires estimating volatility directly from the evolution of the net operating income while accounting for the correlation between the revenue and costs functions. This approach ensures that the uncertainties usually associated with toll revenues, in particular, are thoroughly investigated and their impact on project viability is thoroughly assessed. This paper illustrates the usefulness of the framework with data from an actual (BOT) toll road project. The results show that by postponing the project for a couple of years the project turns out to be viable, whereas it was not without the deferral. The evaluation approach proposed therefore provides a better framework for determining when and the extent of government financial support, if any, that may be needed to support a BOT project on the basis of project economics. The analysis may also be applicable to private sector investment projects, which are characterized by irreversibility and a high rate of uncertainty