Crowdsourced PAC Learning under Classification Noise

In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algorithm that takes unlabeled data points as input and outputs a trained classifier. Our three-step algorithm incorporates majority voting, pure-exploration bandits, and noisy-PAC learning. We prove several guarantees on the number of tasks labeled by workers for PAC learning in this setting and show that our algorithm improves upon the baseline by reducing the total number of tasks given to workers. We demonstrate the robustness of our algorithm by exploring its application to additional realistic crowdsourcing settings.Comment: 14 page

Offset frequency dynamics and phase noise properties of a self-referenced 10 GHz Ti:sapphire frequency comb

This paper shows the experimental details of the stabilization scheme that allows full control of the repetition rate and the carrier-envelope offset frequency of a 10 GHz frequency comb based on a femtosecond Ti:sapphire laser. Octave-spanning spectra are produced in nonlinear microstructured optical fiber, in spite of the reduced peak power associated with the 10 GHz repetition rate. Improved stability of the broadened spectrum is obtained by temperature-stabilization of the nonlinear optical fiber. The carrier-envelope offset frequency and the repetition rate are simultaneously frequency stabilized, and their short- and long-term stabilities are characterized. We also measure the transfer of amplitude noise of the pump source to phase noise on the offset frequency and verify an increased sensitivity of the offset frequency to pump power modulation compared to systems with lower repetition rate. Finally, we discuss merits of this 10 GHz system for the generation of low-phase-noise microwaves

Sphingosine Phosphate Lyase Expression Is Essential for Normal Development in Caenorhabditis elegans

Sphingolipids are ubiquitous membrane constituents whose metabolites function as signaling molecules in eukaryotic cells. Sphingosine 1-phosphate, a key sphingolipid second messenger, regulates proliferation, motility, invasiveness, and programmed cell death. These effects of sphingosine 1-phosphate and similar phosphorylated sphingoid bases have been observed in organisms as diverse as yeast and humans. Intracellular levels of sphingosine 1-phosphate are tightly regulated by the actions of sphingosine kinase, which is responsible for its synthesis and sphingosine-1-phosphate phosphatase and sphingosine phosphate lyase, the two enzymes responsible for its catabolism. In this study, we describe the cloning of the Caenorhabditis elegans sphingosine phosphate lyase gene along with its functional expression in Saccharomyces cerevisiae. Promoter analysis indicates tissue-specific and developmental regulation of sphingosine phosphate lyase gene expression. Inhibition of C. elegans sphingosine phosphate lyase expression by RNA interference causes accumulation of phosphorylated and unphosphorylated long-chain bases and leads to poor feeding, delayed growth, reproductive abnormalities, and intestinal damage similar to the effects seen with exposure to Bacillus thuringiensis toxin. Our results show that sphingosine phosphate lyase is an essential gene in C. elegans and suggest that the sphingolipid degradative pathway plays a conserved role in regulating animal development

Optimally Sparse Frames

Frames have established themselves as a means to derive redundant, yet stable decompositions of a signal for analysis or transmission, while also promoting sparse expansions. However, when the signal dimension is large, the computation of the frame measurements of a signal typically requires a large number of additions and multiplications, and this makes a frame decomposition intractable in applications with limited computing budget. To address this problem, in this paper, we focus on frames in finite-dimensional Hilbert spaces and introduce sparsity for such frames as a new paradigm. In our terminology, a sparse frame is a frame whose elements have a sparse representation in an orthonormal basis, thereby enabling low-complexity frame decompositions. To introduce a precise meaning of optimality, we take the sum of the numbers of vectors needed of this orthonormal basis when expanding each frame vector as sparsity measure. We then analyze the recently introduced algorithm Spectral Tetris for construction of unit norm tight frames and prove that the tight frames generated by this algorithm are in fact optimally sparse with respect to the standard unit vector basis. Finally, we show that even the generalization of Spectral Tetris for the construction of unit norm frames associated with a given frame operator produces optimally sparse frames

Identification of Genes Which Affect Chromosomal Instability (CIN) in a Dosage-Sensitive Manner in <i>Saccharomyces cerevisiae</i>

Chromosomal instability (CIN) refers to circumstances which can alter the chromosomal content of a cell during its division. Aneuploidy is known to be a direct result of CIN but importantly has recently been shown to affect CIN itself. A possible reason as to why aneuploidy could influence CIN is by gene copy-number-variation (CNV) of dosage sensitive genes that are present on the chromosome which was lost or gained by the aneuploid cell. To test this hypothesis, our lab developed a novel form of CIN assay in budding yeast, termed Improved GFP-based Chromosome Transmission Fidelity (iCTF) assay, which allows us to determine the effects that slight copy number changes of individual genes have on CIN in a high-throughput manner. We utilized this assay to systematically screen for genes which can affect the loss rate of a yeast artificial chromosome (YAC) when (1) their copy number was increased by a gene containing plasmid (Over-Dosage CIN) or (2) decreased due to haploid insufficiency (HI-CIN). We identified and validated 36 CIN genes in the Over-Dosage CIN screen as well as 139 CIN genes in the HI-CIN screen. From these 175 CIN genes, in total, only 25 known CIN genes were identified by previous screens, which leave 150 novel CIN genes. Most interestingly, 9 out of 175 CIN gene candidates decrease CIN. To our knowledge this is the first reported case of this phenotype. CIN and aneuploidy are widely known to frequently co-exist in tumorigenic tissues and that they can be caused by loss or gain of certain genes, often involved in maintenance of genomic integrity. The spectrum of such genes is only partially known and it is so far impossible to predict the effects that individual mutations could have on chromosomal instability, especially in such a complex and diverse background as cancer cells. To address this issue we present here a fast and reliable method to determine the effects of single copy number variations in CIN in a quantitative manner