University of Massachusetts Amherst

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.

On September 14, 2015 at 09:50:45 UTC the two detectors of the Laser Interferometer Gravitational-Wave Observatory simultaneously observed a transient gravitational-wave signal. The signal sweeps upwards in frequency from 35 to 250 Hz with a peak gravitational-wave strain of 1.0×10(-21). It matches the waveform predicted by general relativity for the inspiral and merger of a pair of black holes and the ringdown of the resulting single black hole. The signal was observed with a matched-filter signal-to-noise ratio of 24 and a false alarm rate estimated to be less than 1 event per 203,000 years, equivalent to a significance greater than 5.1σ. The source lies at a luminosity distance of 410(-180)(+160) Mpc corresponding to a redshift z=0.09(-0.04)(+0.03). In the source frame, the initial black hole masses are 36(-4)(+5)M⊙ and 29(-4)(+4)M⊙, and the final black hole mass is 62(-4)(+4)M⊙, with 3.0(-0.5)(+0.5)M⊙c(2) radiated in gravitational waves. All uncertainties define 90% credible intervals. These observations demonstrate the existence of binary stellar-mass black hole systems. This is the first direct detection of gravitational waves and the first observation of a binary black hole merger.

Between 1997 June and 2001 February the Two Micron All Sky Survey (2MASS) collected 25.4 Tbytes of raw imaging data covering 99.998% of the celestial sphere in the near-infrared J (1.25 μm), H (1.65 μm), and Ks (2.16 μm) bandpasses. Observations were conducted from two dedicated 1.3 m diameter telescopes located at Mount Hopkins, Arizona, and Cerro Tololo, Chile. The 7.8 s of integration time accumulated for each point on the sky and strict quality control yielded a 10 σ point-source detection level of better than 15.8, 15.1, and 14.3 mag at the J, H, and Ks bands, respectively, for virtually the entire sky. Bright source extractions have 1 σ photometric uncertainty of <0.03 mag and astrometric accuracy of order 100 mas. Calibration offsets between any two points in the sky are <0.02 mag. The 2MASS All-Sky Data Release includes 4.1 million compressed FITS images covering the entire sky, 471 million source extractions in a Point Source Catalog, and 1.6 million objects identified as extended in an Extended Source Catalog.

Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

The third edition of this bestselling resource provides clear, step-by-step guidance for new and experienced interviewers to help them develop, shape, and reflect on interviewing as a qualitative research process. While proposing a phenomenological approach to in-depth interviewing, the author also includes principles and methods that can be adapted to a range of interviewing approaches. Using concrete examples of interviewing techniques to illustrate the issues under discussion, this classic text helps readers to understand the complexities of interviewing and its connections to broader issues of qualitative research. Equally popular for individual and classroom use, the new Third Edition of Interviewing as Qualitative Research features: an introduction to the Institutional Review Board (IRB) process in its historical context, including an expanded discussion of informed consent and its complexities; special attention to the rights of participants in interview research as those rights interact with ethical issues; and, updated references and suggestions for additional reading for a deeper consideration of methodological, ethical, and philosophical issues, including relevant Internet resources.

Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

Conceptual and methodological ambiguities surrounding the concept of perceived behavioral control are clarified. It is shown that perceived control over performance of a behavior, though comprised of separable components that reflect beliefs about self‐efficacy and about controllability, can nevertheless be considered a unitary latent variable in a hierarchical factor model. It is further argued that there is no necessary correspondence between self‐efficacy and internal control factors, or between controllability and external control factors. Self‐efficacy and controllability can reflect internal as well as external factors and the extent to which they reflect one or the other is an empirical question. Finally, a case is made that measures of perceived behavioral control need to incorporate self‐efficacy as well as controllability items that are carefully selected to ensure high internal consistency. Summary and Conclusions Perceived control over performance of a behavior can account for consider‐ able variance in intentions and actions. However, ambiguities surrounding the concept of perceived behavioral control have tended to create uncertainties and to impede progress. The present article attempted to clarify conceptual ambiguities and resolve issues related to the operationalization of perceived behavioral control. Recent research has demonstrated that the overarching concept of perceived behavioral control, as commonly assessed, is comprised of two components: self‐efficacy (dealing largely with the ease or difficulty of performing a behavior) and controllability (the extent to which performance is up to the actor). Contrary to a widely accepted view, it was argued that self‐efficacy expectations do not necessarily correspond to beliefs about internal control factors, and that controllability expectations have no necessary basis in the perceived operation of external factors. Instead, it was suggested that self‐efficacy and controllability may both reflect beliefs about the presence of internal as well as external factors. Rather than making a priori assumptions about the internal or external locus of self‐efficacy and controllability, this issue is best treated as an empirical question. Also of theoretical significance, the present article tried to dispel the notion that self‐efficacy and controllability are incompatible with, or independent of, each other. Although factor analyses of perceived behavioral control items provide clear and consistent evidence for the distinction, there is sufficient commonality between self‐efficacy and controllability to suggest a two‐level hierarchical model. In this model, perceived behavioral control is the overarching, superordinate construct that is comprised of two lower‐level components: self‐efficacy and controllability. This view of the control component in the theory of planned behavior implies that measures of perceived behavioral control should contain items that assess self‐efficacy as well as controllability. Depending on the purpose of the investigation, a decision can be made to aggregate over all items, treating perceived behavioral control as a unitary factor, or to distinguish between self‐efficacy and controllability by entering separate indices into the prediction equation.

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,873 new measurements from 758 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 118 reviews are many that are new or heavily revised, including a new review on Neutrinos in Cosmology.Starting with this edition, the Review is divided into two volumes. Volume 1 includes the Summary Tables and all review articles. Volume 2 consists of the Particle Listings. Review articles that were previously part of the Listings are now included in volume 1.The complete Review (both volumes) is published online on the website of the Particle Data Group (http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is also available.The 2018 edition of the Review of Particle Physics should be cited as: M. Tanabashi et al. (Particle Data Group), Phys. Rev. D 98, 030001 (2018).

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2–7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8–14 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.

BACKGROUND: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. OBJECTIVE: To estimate the length of the incubation period of COVID-19 and describe its public health implications. DESIGN: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. SETTING: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. PARTICIPANTS: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. MEASUREMENTS: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. RESULTS: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. LIMITATION: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. CONCLUSION: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. PRIMARY FUNDING SOURCE: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

Abstract The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.

This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and discussion of current research in the field of machine learning. These proceedings can also be found online at http://www.machinelearning.org. This year, ICML was held July 5..9 at the University of Helsinki, in Helsinki, Finland, and was co-located with COLT-2008, the 21st Annual Conference on Computational Learning Theory, and UAI-2008, the 24th Conference on Uncertainty in Artificial Intelligence. No less than 583 papers were submitted to ICML 2008. There was a very thorough review process, in which each paper was reviewed double-blind by three program committee (PC) members. Authors were able to respond to the initial reviews, and the PC members could then modify their reviews based on online discussions and the content of this author response. There were two discussion periods led by the senior program committee (SPC), one just before and one after the submission of author responses. At the end of the second discussion period, the SPC members gave their recommendations and provided a summary review for each of their papers. Some papers were checked by the SPCs to ensure that reviewer comments had been addressed. Apart from the length restrictions on papers and the compressed time frame, the review process for ICML resembles that of many journal publications. In total, 158 papers were accepted to ICML this year, including a small number of papers which were initially conditionally accepted, yielding an overall acceptance rate of 27%. ICML authors presented their papers both orally and in a poster session, allowing time for detailed discussions with any interested attendees of the conference. Each day of the main conference included one or two invited talks by a prominent researcher. We were very fortunate to be able to host Michael Collins, of the Massachusetts Institute of Technology; Andrew Ng, of Stanford University; and Luc De Raedt, of the Katholieke Universiteit Leuven, and John Winn of Microsoft Research Cambridge. In addition to the technical talks, ICML- 2008 also included nine tutorials held before the main conference, presented by Alex Smola, Arthur Gretton, and Kenji Fukumizu; Bert Kappen and Marc Toussaint; Neil Lawrence; MartinWainwright; Ralf Herbrich and Thore Graepel; Andreas Krause and Carlos Guestrin; Shai Shalev-Shwartz and Yoram Singer; Rob Fergus; and Matthias Seeger. This year our workshops were organized jointly with COLT and UAI as part of a special overlap day, consisting of eleven workshops selected and arranged collaboratively by the respective workshop chairs of the three conferences. This day provided a rich opportunity for interaction among the attendees of the conferences. This year, ICML enlarged its award offerings to match several other well-established conferences. We hope these will help build our community, celebrate our advances, and encourage applications and long-term thinking. In addition to our previously traditional Paper and Student Paper awards, we also gave awards for Application Paper and 10-year Best Paper (for the best paper of ICML 1998, optionally given in conjunction with a co-located conference). We thank the Machine Learning Journal for sponsoring some of our paper awards.

Simulated gastro-intestinal digestion is widely employed in many fields of food and nutritional sciences, as conducting human trials are often costly, resource intensive, and ethically disputable. As a consequence, in vitro alternatives that determine endpoints such as the bioaccessibility of nutrients and non-nutrients or the digestibility of macronutrients (e.g. lipids, proteins and carbohydrates) are used for screening and building new hypotheses. Various digestion models have been proposed, often impeding the possibility to compare results across research teams. For example, a large variety of enzymes from different sources such as of porcine, rabbit or human origin have been used, differing in their activity and characterization. Differences in pH, mineral type, ionic strength and digestion time, which alter enzyme activity and other phenomena, may also considerably alter results. Other parameters such as the presence of phospholipids, individual enzymes such as gastric lipase and digestive emulsifiers vs. their mixtures (e.g. pancreatin and bile salts), and the ratio of food bolus to digestive fluids, have also been discussed at length. In the present consensus paper, within the COST Infogest network, we propose a general standardised and practical static digestion method based on physiologically relevant conditions that can be applied for various endpoints, which may be amended to accommodate further specific requirements. A frameset of parameters including the oral, gastric and small intestinal digestion are outlined and their relevance discussed in relation to available in vivo data and enzymes. This consensus paper will give a detailed protocol and a line-by-line, guidance, recommendations and justifications but also limitation of the proposed model. This harmonised static, in vitro digestion method for food should aid the production of more comparable data in the future.

Both a user's guide and a theoretical exposition of modern detection theory, incorporating recent developments and covering the two major alternative versions of detection theory.

Abstract The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,324 new measurements from 878 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on High Energy Soft QCD and Diffraction and one on the Determination of CKM Angles from B Hadrons. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 98 review articles. Volume 2 consists of the Particle Listings and contains also 22 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print and as a web version optimized for use on phones as well as an Android app.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTGold Nanoparticles in Chemical and Biological SensingKrishnendu Saha, Sarit S. Agasti, Chaekyu Kim, Xiaoning Li, and Vincent M. Rotello*View Author Information Department of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, Massachusetts 01003, United States*E-mail: [email protected]Cite this: Chem. Rev. 2012, 112, 5, 2739–2779Publication Date (Web):February 2, 2012Publication History Received11 April 2011Published online2 February 2012Published inissue 9 May 2012https://pubs.acs.org/doi/10.1021/cr2001178https://doi.org/10.1021/cr2001178review-articleACS PublicationsCopyright © 2012 American Chemical SocietyRequest reuse permissionsArticle Views79826Altmetric-Citations3916LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Electrodes,Genetics,Metal nanoparticles,Peptides and proteins,Sensors Get e-Alerts

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