Training Support Vector Machines Using Frank-Wolfe Optimization Methods

Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale datasets. Traditional optimization methods cannot be directly applied in these cases, mainly due to memory restrictions. By adopting a slightly different objective function and under mild conditions on the kernel used within the model, efficient algorithms to train SVMs have been devised under the name of Core Vector Machines (CVMs). This framework exploits the equivalence of the resulting learning problem with the task of building a Minimal Enclosing Ball (MEB) problem in a feature space, where data is implicitly embedded by a kernel function. In this paper, we improve on the CVM approach by proposing two novel methods to build SVMs based on the Frank-Wolfe algorithm, recently revisited as a fast method to approximate the solution of a MEB problem. In contrast to CVMs, our algorithms do not require to compute the solutions of a sequence of increasingly complex QPs and are defined by using only analytic optimization steps. Experiments on a large collection of datasets show that our methods scale better than CVMs in most cases, sometimes at the price of a slightly lower accuracy. As CVMs, the proposed methods can be easily extended to machine learning problems other than binary classification. However, effective classifiers are also obtained using kernels which do not satisfy the condition required by CVMs and can thus be used for a wider set of problems

Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis.

ObjectiveTo determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)-MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC-MS/MS may be promising tools for predicting response to rituximab

Efficient calibration of four wheel industrial AGVs

In this paper, we propose a novel method for extrinsic and intrinsic automatic calibration of four wheel industrial Automated Guided Vehicles (AGVs) compliant with Ackermann and Dual Drive kinematics. For each kinematic model the algorithm estimates the trajectories measured by an on-board sensor and the expected ones given the state of the wheels. The estimation exploits the model equations derived in this work which constrain calibration parameters and measurements from wheel encoders and sensor odometry. The parameter values are computed through closed-form solutions of least-square estimation. The method has been implemented on Programmable Logic Controllers and tested on industrial AGVs. The developed procedure computes the parameters in about 10−15 minutes, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in a warehouse have assessed the accuracy and stability of the proposed approach. The position accuracy achieved by AGVs calibrated with the proposed method is higher than the one achieved by manual calibration

Phenotypic and Genotypic Characterization of Staphylococcus aureus Strains from Italian Dairy Products

Staphylococcus aureus is a known major cause of foodborne illnesses, and milk and dairy products are often contaminated by enterotoxigenic strains of this bacterium. In the present study, 122 S. aureus isolates collected from different dairy products were characterised by phenotypic properties, by the distribution of genes encoding staphylococcal enterotoxins (sea, sec, sed, seg, seh, sei, sej, and sel) and by randomly amplified polymorphic DNA PCR (RAPD-PCR). Moreover, strain resistance to vancomycin and methicillin (oxacillin) was studied. The differences in the RAPD-PCR profiles obtained with the primers M13 and AP4 revealed the presence of a great genetic heterogeneity among the different S. aureus strains. Using the primer AP4 and M13, eight groups were distinguished by RAPD-PCR cluster analysis, although, except in few cases, it was not possible to correlate the isolates of different animal species (cow or ovine) with the presence of se genes. None of the isolates showed resistance to vancomycin or methicillin