Comment on ``Protective measurements of the wave function of a single squeezed harmonic-oscillator state''

Alter and Yamamoto [Phys. Rev. A 53, R2911 (1996)] claimed to consider ``protective measurements'' [Phys. Lett. A 178, 38 (1993)] which we have recently introduced. We show that the measurements discussed by Alter and Yamamoto ``are not'' the protective measurements we proposed. Therefore, their results are irrelevant to the nature of protective measurements.Comment: 2 pages LaTe

Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Backfiring and favouring : how design processes in HCI lead to anti-patterns and repentant designers

Design is typically envisioned as aiming to improve situations for users, but this can fail. Failure can be the result of flawed design solutions, i.e. anti-patterns. Prior work in anti-patterns has largely focused on their characteristics. We instead concentrate on why they occur by outlining two processes that result in anti-patterns: 1) backfiring, and 2) favouring. The purpose of the paper is to help designers and researchers better understand how design processes can lead to negative impacts and to repentant designers by introducing a richer vocabulary for discussing such processes. We explore how anti-patterns evolve in HCI by specifically applying the vocabulary to examples of social media design. We believe that highlighting these processes will help the HCI community reflect on their own work and also raise awareness of the opportunities for avoiding anti-patterns. Our hope is that this will result in fewer negative experiences for designers and users alike

Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA

Evolutionary relationships among organisms are commonly described by using a hierarchy derived from comparisons of ribosomal RNA (rRNA) sequences. We propose that even on the level of a single rRNA molecule, an organism's evolution is composed of multiple pathways due to concurrent forces that act independently upon different rRNA degrees of freedom. Relationships among organisms are then compositions of coexisting pathway-dependent similarities and dissimilarities, which cannot be described by a single hierarchy. We computationally test this hypothesis in comparative analyses of 16S and 23S rRNA sequence alignments by using a tensor decomposition, i.e., a framework for modeling composite data. Each alignment is encoded in a cuboid, i.e., a third-order tensor, where nucleotides, positions and organisms, each represent a degree of freedom. A tensor mode-1 higher-order singular value decomposition (HOSVD) is formulated such that it separates each cuboid into combinations of patterns of nucleotide frequency variation across organisms and positions, i.e., “eigenpositions” and corresponding nucleotide-specific segments of “eigenorganisms,” respectively, independent of a-priori knowledge of the taxonomic groups or rRNA structures. We find, in support of our hypothesis that, first, the significant eigenpositions reveal multiple similarities and dissimilarities among the taxonomic groups. Second, the corresponding eigenorganisms identify insertions or deletions of nucleotides exclusively conserved within the corresponding groups, that map out entire substructures and are enriched in adenosines, unpaired in the rRNA secondary structure, that participate in tertiary structure interactions. This demonstrates that structural motifs involved in rRNA folding and function are evolutionary degrees of freedom. Third, two previously unknown coexisting subgenic relationships between Microsporidia and Archaea are revealed in both the 16S and 23S rRNA alignments, a convergence and a divergence, conferred by insertions and deletions of these motifs, which cannot be described by a single hierarchy. This shows that mode-1 HOSVD modeling of rRNA alignments might be used to computationally predict evolutionary mechanisms

GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival

Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in 3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

Fidelity trade-off for finite ensembles of identically prepared qubits

We calculate the trade-off between the quality of estimating the quantum state of an ensemble of identically prepared qubits and the minimum level of disturbance that has to be introduced by this procedure in quantum mechanics. The trade-off is quantified using two mean fidelities: the operation fidelity which characterizes the average resemblance of the final qubit state to the initial one, and the estimation fidelity describing the quality of the obtained estimate. We analyze properties of quantum operations saturating the achievability bound for the operation fidelity versus the estimation fidelity, which allows us to reduce substantially the complexity of the problem of finding the trade-off curve. The reduced optimization problem has the form of an eigenvalue problem for a set of tridiagonal matrices, and it can be easily solved using standard numerical tools.Comment: 26 pages, REVTeX, 2 figures. Few minor corrections, accepted for publication in Physical Review

Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli

The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with less changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: 1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation, and 2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog

Examination of the risk of reinfection with hepatitis C among injecting drug users who have been tested in Glasgow

Unsafe injecting practices put injecting drug users (IDUs) at repeat exposure to infection with the hepatitis C virus (HCV). It has not yet been determined if spontaneously clearing one's primary infection influences the risk of reinfection; our aim was to estimate the relative risk of reinfection in IDUs who have cleared the virus. We conducted a retrospective study using a large database of HCV test results covering Greater Glasgow Health Board during 1993–2007 to calculate rates of infection and reinfection in current/former IDUs. The relative risk of (re)infection in previously infected compared with never-infected IDUs was estimated using Poisson regression, adjusting for age at study entry, sex, and calendar period of test. Although the rate of reinfection in IDUs who were HCV antibody-positive, RNA-negative at baseline was lower (7/100 person-years, 95% CI: 5–9) than the rate of acute infection in IDUs who were HCV antibody-negative at baseline (10/100 person-years, 95% CI: 9–12), the risk of reinfection was not significantly different than the risk of initial infection (adjusted rate ratio = 0.78, 95% CI: 0.57–1.08). We found only weak evidence for a reduced risk of HCV reinfection in IDUs who had cleared their previous infection. Further research among those who have cleared infection through antiviral therapy is needed to help inform decisions regarding treatment of IDUs