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GlyQ-IQ is a program that performs a targeted, chromatographic centric search of mass spectral data for glycans. It is designed for high resolution LC-MS datasets. The software uses a list of glycan targets to search for expected features in MS1 spectra. Features are characterized by monoisotopic mass, elution time, and isotopic fit score. Features are annotated by glycan family relationships and optionally by in-source fragmentation patterns.
Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.
On July 14, 1987 NASA's ER-2 high altitude aircraft flew a mission to measure the structure of stratocumulus clouds off the coast of California. A flight pattern was executed so that the two-dimensional variability of the clouds could be detected. The technique of analysis of the lidar data to measure cloud tops follows. First each signal is searched for its maximum in return strength. This maximum is caused by scattering of the laser light off cloud particles or from the ocean surface. Next the variance of the signal return above the level of maximum backscatter is determined. Cloud top is assigned to a level (above the level of maximum backscatter) where the backscatter exceeds the average variance. This two-step process is necessary because the level of maximum backscatter does not correspond to the cloud top. Ocean surface returns are easily separated from cloud returns in this process, described in detail by Boers, Spinhirne, and Hart (1988). Analysis of the data so far has shown that there were very few breaks in the clouds. Furthermore the layer top was very flat with local oscillations not exceeding 30 m. Such small cloud top variations are still well within the range of detectability, because the precision of this technique of cloud top detection has previously been established to be 13 to 15 m. Data are presently being analyzed to compute cloud top distributions and fractional cloudiness. The aim of this research is to relate the fractional cloudiness to the mean thermodynamic structure of the boundary layer. Researchers plan to compute spectral scales of the cloud top variability in two dimensions to determine the orientation of the clouds with respect to the mean wind. Furthermore the lidar derived cloud top distribution will be used in the computation of the thermodynamic and radiation budget of the boundary layer.
We present a dedicated complementarity study of gravitational wave and collider measurements of the simplest extension of the Higgs sector: the singlet scalar augmented Standard Model. We study the following issues: (i) the electroweak phase transition patterns admitted by the model, and the proportion of parameter space for each pattern; (ii) the regions of parameter space that give detectable gravitational waves at future space-based detectors; and (iii) the current and future collider measurements of di-Higgs production, as well as searches for a heavy weak diboson resonance, and how these searches interplay with regions of parameter space that exhibit strong gravitational wave signals. We carefully investigate the behavior of the normalized energy released during the phase transition as a function of the model parameters, address subtle issues pertaining to the bubble wall velocity, and provide a description of different fluid velocity profiles. On the collider side, we identify the subset of points that are most promising in terms of di-Higgs and weak diboson production studies while also giving detectable signals at LISA, setting the stage for future benchmark points that can be used by both communities.
The objective of the Search for Extraterrestrial Intelligence (SETI) is to locate an artificially created signal coming from a distant star. This is done in two steps: (1) spectral analysis of an incoming radio frequency band, and (2) pattern detection for narrow-band signals. Both steps are computationally expensive and require the development of specially designed computer architectures. To reduce the size and cost of the SETI signal detection machine, two custom VLSI chips are under development. The first chip, the SETI DSP Engine, is used in the spectrum analyzer and is specially designed to compute Discrete Fourier Transforms (DFTs). It is a high-speed arithmetic processor that has two adders, one multiplier-accumulator, and three four-port memories. The second chip is a new type of Content-Addressable Memory. It is the heart of an associative processor that is used for pattern detection. Both chips incorporate many innovative circuits and architectural features.
In the past few decades, prediction of macromolecular structures beyond the native conformation has been aided by the development of molecular dynamics (MD) protocols aimed at exploration of the energetic landscape of proteins. Yet, the computed structures do not always agree with experimental observables, calling for further development of the MD strategies to bring the computations and experiments closer together. Here, we report a scalable, efficient MD simulation approach that incorporates an x-ray solution scattering signal as a driving force for the conformational search of stable structural configurations outside of the native basin. We further demonstrate the importance of inclusion of the hydration layer effect for a precise description of the processes involving large changes in the solvent exposed area, such as unfolding. Utilization of the graphics processing unit allows for an efficient all-atom calculation of scattering patterns on-the-fly, even for large biomolecules, resulting in a speed-up of the calculation of the associated driving force. The utility of the methodology is demonstrated on two model protein systems, the structural transition of lysine-, arginine-, ornithine-binding protein and the folding of deca-alanine. We discuss how the present approach will aid in the interpretation of dynamical scattering experiments on protein folding and association.
The enigmatic downchirped signals, called 'perytons', that are detected by radio telescopes in the GHz frequency range may be produced by an atmospheric phenomenon known as ball lightning (BL). If BLs act as nonstationary radio frequency cavities, their characteristic emission frequencies and evolution timescales are consistent with peryton observations, and so are general patterns in which BLs are known to occur. Based on this evidence, testable predictions are made that can confirm or rule out a causal connection between perytons and BLs. In either case, how perytons are searched for in observational data may warrant reconsideration because existing procedures may be discarding events that have the same nature as known perytons.
Abstract We present compact analytic formulae for all one-loop amplitudes representing the production of a Higgs boson in association with two jets, mediated by a colour triplet scalar particle. Many of the integral coefficients present for scalar mediators are identical to the case when a massive fermion circulates in the loop, reflecting a close relationship between the two theories. The calculation is used to study Higgs boson production in association with two jets in a simplified supersymmetry (SUSY) scenario in which the dominant additional contributions arise from loops of top squarks. The results presented here facilitate an indirect search for top squarks in this channel, by a precision measurement of the corresponding cross section. However, we find that the potential for improved discrimination between the Standard Model and SUSY cases suggested by the pattern of results in the one- and two-jet samples is unlikely to be realized due to the loss in statistical power compared to an inclusive analysis.
Microcalorimeters that leverage microwave kinetic inductance detectors to read out phonon signals in the particle-absorbing target, referred to as kinetic inductance phonon-mediated (KIPM) detectors, offer an attractive detector architecture to probe dark matter (DM) down to the fermionic thermal relic mass limit. A prototype KIPM detector featuring a single aluminum resonator patterned onto a 1-gram silicon substrate was operated in the Northwestern EXerimental Underground Site (NEXUS) low-background facility at Fermilab for characterization and evaluation of this detector architecture’s efficacy for a DM search. An energy calibration was performed by exposing the bare substrate to a pulsed source of 470-nm photons, resulting in a baseline resolution on the energy absorbed by the phonon sensor of 2.1 ± 0.2 eV, a factor of two better than the current state of the art, enabled by quasiparticle lifetimes extending up to 6.5 ms. However, due to the subpercent phonon collection efficiency, the resolution on energy deposited in the substrate is limited to σ E = 318 ± 29 eV. We further model both the signal pulse shape as a function of device temperature to extract quasiparticle lifetimes, and the observed noise spectra.
We present Brut, an algorithm to identify bubbles in infrared images of the Galactic midplane. Brut is based on the Random Forest algorithm, and uses bubbles identified by >35,000 citizen scientists from the Milky Way Project to discover the identifying characteristics of bubbles in images from the Spitzer Space Telescope. We demonstrate that Brut's ability to identify bubbles is comparable to expert astronomers. We use Brut to re-assess the bubbles in the Milky Way Project catalog, and find that 10%-30% of the objects in this catalog are non-bubble interlopers. Relative to these interlopers, high-reliability bubbles are more confined to the mid-plane, and display a stronger excess of young stellar objects along and within bubble rims. Furthermore, Brut is able to discover bubbles missed by previous searches—particularly bubbles near bright sources which have low contrast relative to their surroundings. Brut demonstrates the synergies that exist between citizen scientists, professional scientists, and machine learning techniques. In cases where ''untrained' citizens can identify patterns that machines cannot detect without training, machine learning algorithms like Brut can use the output of citizen science projects as input training sets, offering tremendous opportunities to speed the pace of scientific discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone.
The growing complexity of full-scale systems has surpassed the capabilities of most simulation software to provide detailed models or gate-level failure analyses. The process of system-level diagnosis approaches the fault-isolation problem in a manner that differs significantly from the traditional and exhaustive failure mode search. System-level diagnosis is based on a functional representation of the system. For example, one can exercise one portion of a radar algorithm (the Fast Fourier Transform (FFT) function) by injecting several standard input patterns and comparing the results to standardized output results. An anomalous output would point to one of several items (including the FFT circuit) without specifying the gate or failure mode. For system-level repair, identifying an anomalous chip is sufficient. We describe here an information theoretic and dependency modeling approach that discards much of the detailed physical knowledge about the system and analyzes its information flow and functional interrelationships. The approach relies on group and flow associations and, as such, is hierarchical. Its hierarchical nature allows the approach to be applicable to any level of complexity and to any repair level. This approach has been incorporated in a product called STAMP (System Testability and Maintenance Program) which was developed and refined through more than 10 years of field-level applications to complex system diagnosis. The results have been outstanding, even spectacular in some cases. In this paper we describe system-level testability, system-level diagnoses, and the STAMP analysis approach, as well as a few STAMP applications.
Dr. Sargent noted that evidence of other solar systems that might sustain life, particularly human life, is being sought. Protoplanetary (or debris) disks have been observed and are considered evidence that other solar systems exist or are being formed. Also observed is a wobble which is seen as evidence of circulation around a celestial body and gaps that are created by the potential planet. One indicator of life may be these rings or disks of debris around stars. Interferometers, which are telescopic devices that consist of multiple lenses, are being developed in order to better see celestial objects and what may be found around them. Other methods for improving celestial viewing capabilities are also under development. She spoke of particularly looking for wobble and gaps and debris disks where new planets are being formed in an effort to discover another planet that might sustain life as we know it. The next speaker, Dr. Chris McKay, is a planetary scientist at NASA Ames. He talked about the possibility of life on Mars or in some other solar system. He commented on the sameness of the origin of all life, and of the origin of, and the need for, oxygen and water. He believes that water originally came to Earth from comets. At least that is a viable possible source. Water might also have come to Earth via asteroids. Dr. McKay also postulates that there can be no water on Mars because Mars has no plate tectonic system, which he believes is an essential for recycling water. . Dr. Wes Huntress, NASA s Associate Administrator for Space Science, and Dr. Barbara Stone, also from NASA Headquarters, joined Drs. Cordova, Sargent and McKay in the question and answer period following the presentations. (Mr. Goldin was excused to keep an appointment with the President.) The discussion included the following statements and questions: The more missions that there are, the more technology is developed. We need to study our solar system to have something to which we can compare other systems. Before we send people to distant places or to other planets, we need to study the psychological and biological problems that are created by going away from Earth for a long period of time. Pulsars appear to have planets rotating around them. This is of interest and should be studied further. Looking back in time, is there any thought to seeing the development of life? How long did it take for oxygen to rise on the Earth? Do debris disks around the stars provide velocity patterns? To detect life scientists are listening for radio signals, looking for oxygen or ozone, and looking for liquid water. On Earth liquid water is the defining ecological parameter for life. This means that operationally the search for life elsewhere is primarily a search for liquid water.
There has been a surge of interest in hidden valley models with new, strong forces, sometimes called "dark QCD". These models propose asymmetric, composite dark matter in the form of "dark hadrons" that would evade direct and indirect bounds as well as typical collider DM searches for large missing transverse momentum accompanied by radiation. However, evidence of these models can still be found in collider datasets by targeting their unique phenomenological signatures, which include semivisible jets, emerging jets, and soft unclustered energy patterns. We will present the latest experimental results for these signatures and discuss the significant strides in exploring the vast space of dark QCD models. We will further discuss the prospects for dramatic expansions in sensitivity via machine learning.
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.
Assuming a heavy electroweak singlet scalar, which couples to the Standard Model gauge bosons only through loop-induced couplings, SU(2)_L x U(1)_Y gauge invariance imposes interesting patterns on its decays into electroweak gauge bosons, which are dictated by only two free parameters. Therefore experimental measurements on any two of the four possible electroweak channels would determine the remaining two decay channels completely. Furthermore, searches in the WW/ZZ channels probe a complimentary region of parameter space from searches in the gamma-gamma/Z-gamma channels. We derive a model-independent upper bound on the branching fraction in each decay channel, which for the diphoton channel turns out to be about 61%. Including the coupling to gluons, the upper bound on the diphoton branching fraction implies an upper bound on the mass scale of additional colored particles mediating the gluon-fusion production. Using an event rate of about 5 fb for the reported 750 GeV diphoton excess, we find the new colored particle must be lighter than O(1.7 TeV) and O(2.6 TeV) for a pure CP-even and a pure CP-odd singlet scalar, respectively.
The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.
Abstract Eye tracking is a useful tool for studying human cognition, both in the laboratory and in real-world applications. However, there are cases in which eye tracking is not possible, such as in high-security environments where recording devices cannot be introduced. After facing this challenge in our own work, we sought to test the effectiveness of using artificial foveation as an alternative to eye tracking for studying visual search performance. Two groups of participants completed the same list comparison task, which was a computer-based task designed to mimic an inventory verification process that is commonly performed by international nuclear safeguards inspectors. We manipulated the way in which the items on the inventory list were ordered and color coded. For the eye tracking group, an eye tracker was used to assess the order in which participants viewed the items and the number of fixations per trial in each list condition. For the artificial foveation group, the items were covered with a blurry mask except when participants moused over them. We tracked the order in which participants viewed the items by moving their mouse and the number of items viewed per trial in each list condition. We observed the same overall pattern of performance for the various list display conditions, regardless of the method. However, participants were much slower to complete the task when using artificial foveation and had more variability in their accuracy. Our results indicate that the artificial foveation method can reveal the same pattern of differences across conditions as eye tracking, but it can also impact participants’ task performance.