Similarity Learning for Provably Accurate Sparse Linear Classification

In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local k-NN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to state-of-the-art methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

The role of copper in disulfiram-induced toxicity and radiosensitisation of cancer cells.

Abstract Disulfiram has been used for several decades in the treatment of alcoholism. It now shows promise as an anti-cancer drug and radiosensitizer. Proposed mechanisms of action include the induction of oxidative stress and inhibition of proteasome activity. Our purpose was to determine the potential of disulfiram to enhance the anti-tumor efficacy of external beam -irradiation and 131I-metaiodobenzylguanidine (131I-MIBG), a radiopharmaceutical used for the therapy of neuroendocrine tumors. Methods: The role of copper in disulfiram-induced toxicity was investigated by clonogenic assay after treatment of human SK-N-BE(2c) neuroblastoma and UVW/NAT glioma cells. Synergistic interaction between disulfiram and radiotherapy was evaluated by combination index analysis. Tumor growth delay was determined in vitro using multicellular tumor spheroids and in vivo using human tumor xenografts in athymic mice. Results: Escalating disulfiram dosage caused a biphasic reduction in the surviving fraction of clonogens. Clonogenic cell kill after treatment with disulfiram concentrations less than 4 M was copper-dependent, whereas cytotoxicity at concentrations greater than 10 M was caused by oxidative stress. The cytotoxic effect of disulfiram was maximal when administered with equimolar copper. Likewise, disulfiram’s radiosensitization of tumor cells was copper-dependent. Furthermore, disulfiram treatment enhanced the toxicity of 131I-MIBG to spheroids and xenografts expressing the noradrenaline transporter. Conclusions: The results demonstrate that (i) the cytotoxicity of disulfiram was copper-dependent; (ii) molar excess of disulfiram relative to copper resulted in attenuation of disulfiram-mediated cytotoxicity; (iii) copper was required for the radiosensitizing activity of disulfiram and (iv) copper-complexed disulfiram enhanced the efficacy not only of external beam radiation but also of targeted radionuclide therapy in the form of 131I-MIBG. Therefore disulfiram may have anti-cancer potential in combination with radiotherapy

A Super-Dimension Approach in ROLAP Environments

Often the designer of ROLAP applications follows up with the question “can I create a little joiner table with just the two dimension keys and then connect that table to the fact table?” In a classic dimensional model there are two options - (a) both dimensions are modeled independently or (b) two dimensions are combined into a super-dimension with a single key. The second approach is not widely used in ROLAP environments but it is an important sparsity handling method in MOLAP systems. In ROLAP this design technique can also bring storage and performance benefits, although the model becomes more complicated. The dependency between dimensions is a key factor that the designers have to consider when choosing between the two options. In this paper we present the results of our storage and performance experiments over a real life data cubes in reference to these design approaches. Some conclusions are drawn

Comparative study of gearbox fault diagnosis by vibration measurements

Vibration analysis has been demonstrated to be one of the best tools to detect faults in a gearbox by providing abundant information about the operating condition of a gearbox. However, a gearbox generates complex vibration signals, which makes it difficult to diagnose when a fault occurs. There are several fault diagnosis methods that can be utilized to analyze the underlying signals. The time-frequency method has been used and showed some promising results. On the other hand, it also has its drawback when it is applied to a complex mechanical system such as gearboxes. This paper thus attempts to examine the effectiveness of several diagnosis methods to detect faults in a gearbox from vibration measurements. The results show that the cepstrum method can provide a more accurate indication of a faulty gearbox compared to other diagnosis methods