Learning with SGD and Random Features

Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms. In this paper, we investigate their application in the context of nonparametric statistical learning. More precisely, we study the estimator defined by stochastic gradient with mini batches and random features. The latter can be seen as form of nonlinear sketching and used to define approximate kernel methods. The considered estimator is not explicitly penalized/constrained and regularization is implicit. Indeed, our study highlights how different parameters, such as number of features, iterations, step-size and mini-batch size control the learning properties of the solutions. We do this by deriving optimal finite sample bounds, under standard assumptions. The obtained results are corroborated and illustrated by numerical experiments

On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adapted to the problem at hand. Yet, performing leverage scores sampling is a challenge in its own right requiring further approximations. In this paper, we study the problem of leverage score sampling for positive definite matrices defined by a kernel. Our contribution is twofold. First we provide a novel algorithm for leverage score sampling and second, we exploit the proposed method in statistical learning by deriving a novel solver for kernel ridge regression. Our main technical contribution is showing that the proposed algorithms are currently the most efficient and accurate for these problems

Statistical limits of supervised quantum learning

Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms for supervised learning—for which statistical guarantees are available—cannot achieve polylogarithmic runtimes in the input dimension. We conclude that, when no further assumptions on the problem are made, quantum machine learning algorithms for supervised learning can have at most polynomial speedups over efficient classical algorithms, even in cases where quantum access to the data is naturally available

Fermentation Characteristics and Nitrogen Retention of Madura Cattle Fed Complete Rations Containing Soybean Pod and By-Products

This study was aimed to evaluate the effect of complete rations containing soybean pod and soybean by-products (soybean meal and tofu waste) on rumen microbial population, fermentation characteristics, nutrient digestibility, and nitrogen retention of Madura cattle. Twelve Madura cattle of 1.5 years of age were given 4 feeding treatments in triplicates in randomized block design experiment. The treatments included T0 (100% native grass) as a negative control, T1 (concentrate: grass (60:40) as a positive control, T2 (complete ration containing 15% soybean pods), and T3 (complete ration containing 30% soybean pods). The treatments were based on feeding practices commonly applied by farmers in the village. The results showed that the use of concentrate rations or complete rations containing soybean pod and by-product did not affect protozoa population, ammonia concentration, and total VFA production compared to cattle fed 100% native grass. In contrast, the use of concentrate rations or complete rations containing soybean pod and by-products reduced acetate and increased butyrate proportion compared to native grass. The use of a concentrate ration resulted the highest propionate proportion. Methane estimation increased with the use of concentrate ration or complete ration containing 15% soybean pod, but it decreased when the level of soybean pod was increased to 30%. It can be concluded that soybean pod has a potential to be used as a fiber source in beef cattle ration to substitute native grass

Manifold Structured Prediction

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our stud

Manifold structured prediction

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold-valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study

Isolation of Candidate Genes Involved in Cold Temperatures Response in \u3cem\u3eFestuca Pratensis\u3c/em\u3e Huds., Using Suppression Subtractive Hybridisation and Microarray Approaches

The objective of this work was to isolate candidate genes which are differentially expressed following cold-acclimation and develop SNPs to test for associations between candidate genes and frost tolerance. The ability to develop sufficient levels of tolerance against freezing temperatures through cold-acclimation (hardening) is crucial for survival of grasses and winter cereals in temperate climate. Meadow fescue (Festauca pratensis Huds.) is one of the most important forage grass species in Northern Europe. The preference of Festuca instead of Lolium in Norway is due to its superior combination of winter hardiness and forage quality

One pot ‘click’ reactions: tandem enantioselective biocatalytic epoxide ring opening and [3+2] azide alkyne cycloaddition

Halohydrin dehalogenase (HheC) can perform enantioselective azidolysis of aromatic epoxides to 1,2-azido alcohols which are subsequently ligated to alkynes producing chiral hydroxy triazoles in a one-pot procedure with excellent enantiomeric excess.

POTENSI EKSTRAK ORGAN VEGETATIF ANGGREK VANDA HASIL PERSILANGAN SEBAGAI AGEN ANTIKANKER

Penelitian ini bertujuan untuk menguji sitotoksisitas fraksi ekstrak kloroform gabungan organ vegetatif Vanda hasil persilangan terhadap kanker payudara cell line T47D dan kanker serviks cell line HeLa. Organ vegetatif dari tanaman tersebut diekstrak dan difraksinasi. Hasil fraksinasi dengan profil yang sama digabungkan. Fraksi gabungan diuji sitotoksisitasnya terhadap cell line T47D dan cell line HeLa menggunakan metode MTT Assay. Analisis data untuk penentuan IC50 dihitung dengan analisis probit dalam SPSS 17. Hasil penelitian menunjukan fraksi C yang paling baik dalam menghambat pertumbuhan sel kanker payudara cell line T47D dan sel kanker serviks cell line HeLa

Approximating Hamiltonian dynamics with the Nyström method

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms for the approximation of Hamiltonian dynamics using subsampling methods from randomized numerical linear algebra. We derive a simulation technique whose runtime scales polynomially in the number of qubits and the Frobenius norm of the Hamiltonian. As an immediate application, we show that sample based quantum simulation, a type of evolution where the Hamiltonian is a density matrix, can be efficiently classically simulated under specific structural conditions. Our main technical contribution is a randomized algorithm for approximating Hermitian matrix exponentials. The proof leverages a low-rank, symmetric approximation via the Nyström method. Our results suggest that under strong sampling assumptions there exist classical poly-logarithmic time simulations of quantum computations