Robust Plackett–Luce model for k-ary crowdsourced preferences

© 2017, The Author(s). The aggregation of k-ary preferences is an emerging ranking problem, which plays an important role in several aspects of our daily life, such as ordinal peer grading and online product recommendation. At the same time, crowdsourcing has become a trendy way to provide a plethora of k-ary preferences for this ranking problem, due to convenient platforms and low costs. However, k-ary preferences from crowdsourced workers are often noisy, which inevitably degenerates the performance of traditional aggregation models. To address this challenge, in this paper, we present a RObust PlAckett–Luce (ROPAL) model. Specifically, to ensure the robustness, ROPAL integrates the Plackett–Luce model with a denoising vector. Based on the Kendall-tau distance, this vector corrects k-ary crowdsourced preferences with a certain probability. In addition, we propose an online Bayesian inference to make ROPAL scalable to large-scale preferences. We conduct comprehensive experiments on simulated and real-world datasets. Empirical results on “massive synthetic” and “real-world” datasets show that ROPAL with online Bayesian inference achieves substantial improvements in robustness and noisy worker detection over current approaches

Riemannian pursuit for big matrix recovery

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of existing methods, however, deteriorates significantly when applied to ill-conditioned or large-scale matrices. In this paper, we therefore propose an efficient method, called Riemannian Pursuit (RP), that aims to address these two problems simultaneously. Our method consists of a sequence of fixed-rank optimization problems. Each subproblem, solved by a nonlinear Rieman-nian conjugate gradient method, aims to correct the solution in the most important subspace of increasing size. Theoretically, RP converges linearly under mild conditions and experimental results show that it substantially outperforms existing methods when applied to large-scale and ill-conditioned matrices

Model dan Mekanisme Pengelolaan Kebun Benih Tanaman Hutan Bersertifikat di Perum Perhutani Unit II Jawa Timur dan Puslitbang Perhutani Cepu

The purpose of this research is to find out: (1) the model and mechanism of forest management as the certificated seed nursery forest plant; (2) the model and mechanism of production of the certificated seed nursery forest plant; (3) the model and mechanism of certification of seed nursery, seed, and forest plant seed; and (4) the model and mechanism of forest plant seed and germ marketing at Perum Perhutani Unit II of East Java and Puslitbang Perhutani of Cepu. This research results shows that the mechanism of the certificated seed nursery forest plant management is carried out by maintaining and developing the genetic resources at all working units of Perum Perhutani and the plant glorification is carried out by Puslitbang Perhutani of Cepu. Seed produced is used to produce plant, protected forest rehabilitation and to be marketed. The mechanism of seed and germ production is carried out according to the procedure operational standard. The mechanism of seed nursery, seed and germ of Perum Perhutani is carried out by submitting an application of certification to the Balai Perbenihan Tananamn Hutan Jawa Madura (The Office of Forest Plant Seedling of Java Madura). The mechanism of seed and germ marketing at Perum Perhutani Unit II of East Java is carried out by Kesatuan Bisnis Mandiri (KBM) Agroforestry (Agroforestry Autonomous Business Unit), Puslitbang Perhutani of Cepu, and Kesatuan Pengelolaan Hutan (KPH) (The Forest Management Unit)

Evaluation of the initial implementation of a nationwide diabetic retinopathy screening programme in primary care: A multimethod study

Objectives The Australian Government funded a nationwide diabetic retinopathy screening programme to improve visual outcomes for people with diabetes. This study examined the benefits and barriers of the programme, image interpretation pathways and assessed the characteristics of people who had their fundus photos graded by a telereading service which was available as a part of the programme. Design Multimethod: survey and retrospective review of referral forms. Setting Twenty-two primary healthcare facilities from urban, regional, rural and remote areas of Australia, and one telereading service operated by a referral-only eye clinic in metropolitan Sydney, Australia. Participants Twenty-seven primary healthcare workers out of 110 contacted completed a survey, and 145 patient referrals were reviewed. Results Manifest qualitative content analysis showed that primary healthcare workers reported that the benefits of the screening programme included improved patient outcomes and increased awareness and knowledge of diabetic retinopathy. Barriers related to staffing issues and limited referral pathways. Image grading was performed by a variety of primary healthcare workers, with one responder indicating the utilisation of a diabetic retinopathy reading service. Of the people with fundus photos graded by the reading service, 26.2% were reported to have diabetes. Overall, 12.3% of eyes were diagnosed with diabetic retinopathy. Photo quality was rated as excellent in 46.2% of photos. Referral to an optometrist for diabetic retinopathy was recommended in 4.1% of cases, and to an ophthalmologist in 6.9% of cases. Conclusions This nationwide diabetic retinopathy screening programme was perceived to increase access to diabetic retinopathy screening in regional, rural and remote areas of Australia. The telereading service has diagnosed diabetic retinopathy and other ocular pathologies in images it has received. Key barriers, such as access to ophthalmologists and optometrists, must be overcome to improve visual outcomes

Fully-Unintegrated Parton Distribution and Fragmentation Functions at Perturbative k_T

We define and study the properties of generalized beam functions (BFs) and fragmenting jet functions (FJFs), which are fully-unintegrated parton distribution functions (PDFs) and fragmentation functions (FFs) for perturbative k_T. We calculate at one loop the coefficients for matching them onto standard PDFs and FFs, correcting previous results for the BFs in the literature. Technical subtleties when measuring transverse momentum in dimensional regularization are clarified, and this enables us to renormalize in momentum space. Generalized BFs describe the distribution in the full four-momentum k_mu of a colliding parton taken out of an initial-state hadron, and therefore characterize the collinear initial-state radiation. We illustrate their importance through a factorization theorem for pp -> l^+ l^- + 0 jets, where the transverse momentum of the lepton pair is measured. Generalized FJFs are relevant for the analysis of semi-inclusive processes where the full momentum of a hadron, fragmenting from a jet with constrained invariant mass, is measured. Their significance is shown for the example of e^+ e^- -> dijet+h, where the perpendicular momentum of the fragmenting hadron with respect to the thrust axis is measured.Comment: Journal versio

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.Comment: NIPS 2018 camera-ready versio

Strong and Tunable Nonlinear Optomechanical Coupling in a Low-Loss System

A major goal in optomechanics is to observe and control quantum behavior in a system consisting of a mechanical resonator coupled to an optical cavity. Work towards this goal has focused on increasing the strength of the coupling between the mechanical and optical degrees of freedom; however, the form of this coupling is crucial in determining which phenomena can be observed in such a system. Here we demonstrate that avoided crossings in the spectrum of an optical cavity containing a flexible dielectric membrane allow us to realize several different forms of the optomechanical coupling. These include cavity detunings that are (to lowest order) linear, quadratic, or quartic in the membrane's displacement, and a cavity finesse that is linear in (or independent of) the membrane's displacement. All these couplings are realized in a single device with extremely low optical loss and can be tuned over a wide range in situ; in particular, we find that the quadratic coupling can be increased three orders of magnitude beyond previous devices. As a result of these advances, the device presented here should be capable of demonstrating the quantization of the membrane's mechanical energy.Comment: 12 pages, 4 figures, 1 tabl

A general method for the resummation of event-shape distributions in e⁺ e− annihilation

We present a novel method for resummation of event shapes to next-to-next-to-leading-logarithmic (NNLL) accuracy. We discuss the technique and describe its implementation in a numerical program in the case of e + e − collisions where the resummed prediction is matched to NNLO. We reproduce all the existing predictions and present new results for oblateness and thrust major

Amyloid formation: interface influence

The causes of pathological conditions such as Alzheimer’s and Parkinson’s diseases are becoming better understood. Proteins that misfold from their native structure to form aggregates of β-sheet fibrils — termed amyloid — are known1,2 to be implicated in these ‘amyloid diseases’. Understanding the early steps of fibril formation is critical, and the conditions, mechanism and kinetics of protein and peptide aggregation are being widely investigated through a variety of in vitro studies. Kinetic aspects of the dispersion of the protein or peptide in solution are thought to influence the fibrillization process by mass-transfer effects. In addition, mixing also leads to shear forces, which can influence fibril growth by perturbing the equilibrium between the isolated and aggregated proteins, causing existing fibrils to fragment and create new nuclei3. Writing in the Journal of the American Chemical Society, David Talaga and co-workers have now highlighted4 an additional factor that can influence the fibrillization of amyloid-forming proteins — the presence of hydrophobic interfaces