On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Opportunities and challenges grow from Arabidopsis genome sequencing

A recent Cold Spring Harbor Laboratory meeting in December 1997 provided the first meeting on the Arabidopsisgenome featuring a unique combination of functional studies and sequencing efforts; it included a broad range of talks covering genome sequencing and analysis efforts, mapping and defining genes, and gene expression patterns and function. Significant points to come out of the meeting were that a number of international consortiums have completed substantial portions of sequence on all five chromosomes with 17 Mb of sequence currently available through various web pages and 8 Mb of annotated sequence available through GenBank. Although physical maps of three of the five chromosomes have not yet been completed, David Bouchez (INRA, Versailles, France) reported that >90% of the clones in the CIC (CNRS, INRA, CEPH) Arabidopsis YAC library have been anchored via hybridization to genetically mapped markers. This should greatly facilitate the construction of physical maps. Michael Mindrinos from the Ausubel laboratory (Massachusetts General Hospital, Boston, MA) reported the development of a new class of PCR-based marker, the SNAPs (single nucleotideamplified polymorphisms), which should greatly assist positional cloning efforts. Daphne Preuss (University of Chicago, IL) reported the use of tetrad analysis to place the centromeres on the genetic map (Fig. 1), taking advantage of the pollen mutant quartet1 (Preuss et al. 1994; Copenhaver et al. 1998). Interestingly, this analysis placed the centromeres very close to, but not necessarily within, the centromeric repeat blocks mapped recently by Round et al. (1997)

Optimism in Active Learning with Gaussian Processes

International audienceIn the context of Active Learning for classification, the classification error depends on the joint distribution of samples and their labels which is initially unknown. The minimization of this error requires estimating this distribution. Online estimation of this distribution involves a trade-off between exploration and exploitation. This is a common problem in machine learning for which multi-armed bandit theory, building upon Optimism in the Face of Uncertainty, has been proven very efficient these last years. We introduce two novel algorithms that use Optimism in the Face of Uncertainty along with Gaussian Processes for the Active Learning problem. The evaluation lead on real world datasets shows that these new algorithms compare positively to state-of-the-art methods

A Monte Carlo study of the three-dimensional Coulomb frustrated Ising ferromagnet

We have investigated by Monte-Carlo simulation the phase diagram of a three-dimensional Ising model with nearest-neighbor ferromagnetic interactions and small, but long-range (Coulombic) antiferromagnetic interactions. We have developed an efficient cluster algorithm and used different lattice sizes and geometries, which allows us to obtain the main characteristics of the temperature-frustration phase diagram. Our finite-size scaling analysis confirms that the melting of the lamellar phases into the paramgnetic phase is driven first-order by the fluctuations. Transitions between ordered phases with different modulation patterns is observed in some regions of the diagram, in agreement with a recent mean-field analysis.Comment: 14 pages, 10 figures, submitted to Phys. Rev.

Non-exponential kinetic behavior of confined water

We present the results of molecular dynamics simulations of SPC/E water confined in a realistic model of a silica pore. The single-particle dynamics have been studied at ambient temperature for different hydration levels. The confinement near the hydrophilic surface makes the dynamic behaviour of the liquid strongly dependent on the hydration level. Upon decrease of the number of water molecules in the pore we observe the onset of a slow dynamics due to the ``cage effect''. The conventional picture of a stochastic single-particle diffusion process thus looses its validity

Rectal Microbiome Composition Correlates with Humoral Immunity to HIV-1 in Vaccinated Rhesus Macaques.

The microbiome is an integral and dynamic component of the host and is emerging as a critical determinant of immune responses; however, its influence on vaccine immunogenicity is largely not well understood. Here, we examined the pivotal relationship between the mucosal microbiome and vaccine-induced immune responses by assessing longitudinal changes in vaginal and rectal microbiome profiles after intradermal immunization with a human immunodeficiency virus type 1 (HIV-1) DNA vaccine in adult rhesus macaques that received two prior DNA primes. We report that both vaginal and rectal microbiomes were dominated by Firmicutes but were composed of distinct genera, denoting microbiome specialization across mucosal tissues. Following immunization, the vaginal microbiome was resilient, except for a transient decrease in Streptococcus In contrast, the rectal microbiome was far more responsive to vaccination, exhibiting an increase in the ratio of Firmicutes to Bacteroidetes Within Bacteroidetes, multiple genera were significantly decreased, including Prevotella, Alloprevotella, Bacteroides, Acetobacteroides, Falsiporphyromonas, and Anaerocella. Decreased abundance of Prevotella correlated with induction of gut-homing α4β7 + effector CD4 T cells. Prevotella abundance also negatively correlated with rectal HIV-1 specific IgG levels. While rectal Lactobacillus was unaltered following DNA vaccination, baseline Lactobacillus abundance showed strong associations with higher rectal HIV-1 gp140 IgA induced following a protein boost. Similarly, the abundance of Clostridium in cluster IV was associated with higher rectal HIV-1 gp140 IgG responses. Collectively, these data reveal that the temporal stability of bacterial communities following DNA immunization is site dependent and highlight the importance of host-microbiome interactions in shaping HIV-1 vaccine responses. Our findings have significant implications for microbial manipulation as a strategy to enhance HIV vaccine-induced mucosal immunity.IMPORTANCE There is considerable effort directed toward evaluating HIV-1 vaccine platforms to select the most promising candidates for enhancing mucosal HIV-1 antibody. The most successful thus far, the RV144 trial provided partial protection due to waning HIV-1 antibody titers. In order to develop an effective HIV vaccine, it may therefore be important to understand how biological factors, such as the microbiome, modulate host immune responses. Furthermore, as intestinal microbiota antigens may generate antibodies cross-reactive to the HIV-1 envelope glycoprotein, understanding the relationship between gut microbiota composition and HIV-1 envelope antibody responses after vaccination is important. Here, we demonstrate for the first time in rhesus macaques that the rectal microbiome composition can influence HIV-1 vaccine immunogenicity, and we report temporal changes in the mucosal microbiome profile following HIV-1 vaccination. Our results could inform findings from the HIV Vaccine Trials Network (HVTN) vaccine studies and contribute to an understanding of how the microbiome influences HIV-1 antibody responses

An analysis of a manufacturing process using the GERT approach

Graphical Evaluation and Review Technique for analyzing manufacturing processe

A Probe of New Physics in Top Quark Pair Production at e−e+e^-e^+ Colliders

We describe how to probe new physics through examination of the form factors describing the Ztt couplings via the scattering process e^-e^+->t+tbar. We focus on experimental methods on how the top quark momentum can be determined and show how this can be applied to select polarized samples of $t\bar{t}$ pairs through the angular correlations in the final state leptons. We also study the dependence on the energy and luminosity of an \ee\ collider to probe a CP violating asymmetry at the $10^{-2}$ level.}Comment: 24 pages in TeXsis (figures available upon request) (revised July 1993

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page