Ethical issues of concern in the USDA-ARS (Chapter 3)

Widely publicized occurrences and allegations of fraud and plagiarism in scientific publications have eroded public confidence in the integrity of scientists. They have caused scientists to question the wisdom of our traditional reliance on the honor system and the self-correcting nature of the process. Concerns about such misconduct have also raised questions about the ethical climate in our scientific institutions and how to improve it. One important way institutions establish and maintain their ethical climate is through their publication policies. Although allegations or instances of scientific misconduct in the Agricultural Research Service (ARS) have been few, it is currently reviewing its ethical climate and procedures for dealing with scientific misconduct, reflecting science and society's general concern. In ARS, classification (rank, promotion, and demotion) and annual performance appraisals of research scientists are based largely on accomplishments documented in scientific publications. There is a pervasive trend among scientists both within and outside ARS toward summarizing achievement in terms of numbers of papers published. It is easier to count publications than to objectively assess their quality and impact. Procedures used to assess quality and impact of publications rely heavily on formal peer review of publications during the classification process. Therefore, continued reinforcement is required to keep the focus on quality and impact during review. Manuscripts reporting original research are also peer reviewed within ARS before they are approved by ARS for submission to journals. The ARS is developing a Code of Scientific Ethics to emphasize ethical responsibilities and aspirations relevant to its activities. Procedures for dealing with allegations and instances of data falsification and plagiarism are under review and an ARS directive formally defining the procedures is being developed. It is anticipated that both the code of ethics and the directive for dealing with misconduct in science will be officially adopted by ARS in 1992

Theory of the tunneling resonances of the bilayer electron systems in strong magnetic field

We develop a theory for the anomalous interlayer conductance peaks observed in bilayer electron systems at nu=1. Our model shows the that the size of the peak at zero bias decreases rapidly with increasing in-plane magnetic field, but its location is unchanged. The I-V characteristic is linear at small voltages, in agreement with experimental observations. In addition we make quantitative predictions for how the inter-layer conductance peaks vary in position with in-plane magnetic field at high voltages. Finally, we predict novel bi-stable behavior at intermediate voltages.Comment: 5 pages, 2 figure

On the topological classification of binary trees using the Horton-Strahler index

The Horton-Strahler (HS) index $r=\max{(i,j)}+\delta_{i,j}$ has been shown to be relevant to a number of physical (such at diffusion limited aggregation) geological (river networks), biological (pulmonary arteries, blood vessels, various species of trees) and computational (use of registers) applications. Here we revisit the enumeration problem of the HS index on the rooted, unlabeled, plane binary set of trees, and enumerate the same index on the ambilateral set of rooted, plane binary set of trees of $n$ leaves. The ambilateral set is a set of trees whose elements cannot be obtained from each other via an arbitrary number of reflections with respect to vertical axes passing through any of the nodes on the tree. For the unlabeled set we give an alternate derivation to the existing exact solution. Extending this technique for the ambilateral set, which is described by an infinite series of non-linear functional equations, we are able to give a double-exponentially converging approximant to the generating functions in a neighborhood of their convergence circle, and derive an explicit asymptotic form for the number of such trees.Comment: 14 pages, 7 embedded postscript figures, some minor changes and typos correcte

Development of a graphical user interface for automatic separation of human voice from Doppler ultrasound audio in diving experiments

Doppler ultrasound (DU) is used in decompression research to detect venous gas emboli in the precordium or subclavian vein, as a marker of decompression stress. This is of relevance to scuba divers, compressed air workers and astronauts to prevent decompression sickness (DCS) that can be caused by these bubbles upon or after a sudden reduction in ambient pressure. Doppler ultrasound data is graded by expert raters on the Kisman-Masurel or Spencer scales that are associated to DCS risk. Meta-analyses, as well as efforts to computer-automate DU grading, both necessitate access to large databases of well-curated and graded data. Leveraging previously collected data is especially important due to the difficulty of repeating large-scale extreme military pressure exposures that were conducted in the 70-90s in austere environments. Historically, DU data (Non-speech) were often captured on cassettes in one-channel audio with superimposed human speech describing the experiment (Speech). Digitizing and separating these audio files is currently a lengthy, manual task. In this paper, we develop a graphical user interface (GUI) to perform automatic speech recognition and aid in Non-speech and Speech separation. This constitutes the first study incorporating speech processing technology in the field of diving research. If successful, it has the potential to significantly accelerate the reuse of previously-acquired datasets. The recognition task incorporates the Google speech recognizer to detect the presence of human voice activity together with corresponding timestamps. The detected human speech is then separated from the audio Doppler ultrasound within the developed GUI. Several experiments were conducted on recently digitized audio Doppler recordings to corroborate the effectiveness of the developed GUI in recognition and separations tasks, and these are compared to manual labels for Speech timestamps. The following metrics are used to evaluate performance: the average absolute differences between the reference and detected Speech starting points, as well as the percentage of detected

Dynamic ordering and frustration of confined vortex rows studied by mode-locking experiments

The flow properties of confined vortex matter driven through disordered mesoscopic channels are investigated by mode locking (ML) experiments. The observed ML effects allow to trace the evolution of both the structure and the number of confined rows and their match to the channel width as function of magnetic field. From a detailed analysis of the ML behavior for the case of 3-rows we obtain ({\it i}) the pinning frequency $f_p$, ({\it ii}) the onset frequency $f_c$ for ML ($\propto$ ordering velocity) and ({\it iii}) the fraction $L_{ML}/L$ of coherently moving 3-row regions in the channel. The field dependence of these quantities shows that, at matching, where $L_{ML}$ is maximum, the pinning strength is small and the ordering velocity is low, while at mismatch, where $L_{ML}$ is small, both the pinning force and the ordering velocity are enhanced. Further, we find that $f_c \propto f_p^2$, consistent with the dynamic ordering theory of Koshelev and Vinokur. The microscopic nature of the flow and the ordering phenomena will also be discussed.Comment: 10 pages, 7 figure, submitted to PRB. Discussion has been improved and a figure has been adde

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

Doppler ultrasound (DU) measurements are used to detect and evaluate venous gas emboli (VGE) formed after decompression. Automated methodologies for assessing VGE presence using signal processing have been developed on varying real-world datasets of limited size and without ground truth values preventing objective evaluation. We develop and report a method to generate synthetic post-dive data using DU signals collected in both precordium and subclavian vein with varying degrees of bubbling matching field-standard grading metrics. This method is adaptable, modifiable, and reproducible, allowing for researchers to tune the produced dataset for their desired purpose. We provide the baseline Doppler recordings and code required to generate synthetic data for researchers to reproduce our work and improve upon it. We also provide a set of pre-made synthetic post-dive DU data spanning six scenarios representing the Spencer and Kisman-Masurel (KM) grading scales as well as precordial and subclavian DU recordings. By providing a method for synthetic post-dive DU data generation, we aim to improve and accelerate the development of signal processing techniques for VGE analysis in Doppler ultrasound

Modeling the Subsurface Structure of Sunspots

While sunspots are easily observed at the solar surface, determining their subsurface structure is not trivial. There are two main hypotheses for the subsurface structure of sunspots: the monolithic model and the cluster model. Local helioseismology is the only means by which we can investigate subphotospheric structure. However, as current linear inversion techniques do not yet allow helioseismology to probe the internal structure with sufficient confidence to distinguish between the monolith and cluster models, the development of physically realistic sunspot models are a priority for helioseismologists. This is because they are not only important indicators of the variety of physical effects that may influence helioseismic inferences in active regions, but they also enable detailed assessments of the validity of helioseismic interpretations through numerical forward modeling. In this paper, we provide a critical review of the existing sunspot models and an overview of numerical methods employed to model wave propagation through model sunspots. We then carry out an helioseismic analysis of the sunspot in Active Region 9787 and address the serious inconsistencies uncovered by \citeauthor{gizonetal2009}~(\citeyear{gizonetal2009,gizonetal2009a}). We find that this sunspot is most probably associated with a shallow, positive wave-speed perturbation (unlike the traditional two-layer model) and that travel-time measurements are consistent with a horizontal outflow in the surrounding moat.Comment: 73 pages, 19 figures, accepted by Solar Physic

Global Search for New Physics with 2.0/fb at CDF

Data collected in Run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (Vista) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps" that could indicate resonant production of new particles; and the Sleuth procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0/fb of ppbar collisions at sqrt(s)=1.96 TeV reveals no indication of physics beyond the standard model.Comment: 8 pages, 7 figures. Final version which appeared in Physical Review D Rapid Communication

Observation of Orbitally Excited B_s Mesons

We report the first observation of two narrow resonances consistent with states of orbitally excited (L=1) B_s mesons using 1 fb^{-1} of ppbar collisions at sqrt{s} = 1.96 TeV collected with the CDF II detector at the Fermilab Tevatron. We use two-body decays into K^- and B^+ mesons reconstructed as B^+ \to J/\psi K^+, J/\psi \to \mu^+ \mu^- or B^+ \to \bar{D}^0 \pi^+, \bar{D}^0 \to K^+ \pi^-. We deduce the masses of the two states to be m(B_{s1}) = 5829.4 +- 0.7 MeV/c^2 and m(B_{s2}^*) = 5839.7 +- 0.7 MeV/c^2.Comment: Version accepted and published by Phys. Rev. Let

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization