Quantifying Cognitive Processes in Virtual Learning Simulations

Virtual learning simulations have received increasing attention due various proposed educational, instructional, and institutional advantages; with literature focusing largely on perceptions of this technology and empirical comparisons to other instructional methods. Compared to traditional learning environments, virtual learning environments may present methodological advantages in studying learning processes through applying behavioral tracing techniques. This paper will discuss behavioral indicators of cognitive learning processes used in virtual decision scenarios designed for third year engineering and engineering technology students. Behavioral measures to quantitatively analyze the learning process will be presented. Implications for assessing student learning, instructional strategy selection, and improving higher education quality will be shared from holistic perspective

Owner perceptions of their cat's quality of life when treated with a modified University of Wisconsin-Madison protocol for lymphoma

The objectives of this study were to assess owner perceptions of their cat’s quality of life during treatment for lymphoma with a doxorubicin-containing multi-agent chemotherapy protocol, whether various health-related parameters correlated with quality of life scores, and to assess owner satisfaction with the protocol

A continental phenology model for monitoring vegetation responses to interannual climatic variability

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km × 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990–1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability

Parameterization and Sensitivity Analysis of the BIOME-BGC Terrestrial Ecosystem model: Net Primary Production Controls

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed

Automated Quantum Oracle Synthesis with a Minimal Number of Qubits

Several prominent quantum computing algorithms--including Grover's search algorithm and Shor's algorithm for finding the prime factorization of an integer--employ subcircuits termed 'oracles' that embed a specific instance of a mathematical function into a corresponding bijective function that is then realized as a quantum circuit representation. Designing oracles, and particularly, designing them to be optimized for a particular use case, can be a non-trivial task. For example, the challenge of implementing quantum circuits in the current era of NISQ-based quantum computers generally dictates that they should be designed with a minimal number of qubits, as larger qubit counts increase the likelihood that computations will fail due to one or more of the qubits decohering. However, some quantum circuits require that function domain values be preserved, which can preclude using the minimal number of qubits in the oracle circuit. Thus, quantum oracles must be designed with a particular application in mind. In this work, we present two methods for automatic quantum oracle synthesis. One of these methods uses a minimal number of qubits, while the other preserves the function domain values while also minimizing the overall required number of qubits. For each method, we describe known quantum circuit use cases, and illustrate implementation using an automated quantum compilation and optimization tool to synthesize oracles for a set of benchmark functions; we can then compare the methods with metrics including required qubit count and quantum circuit complexity.Comment: 18 pages, 10 figures, SPIE Defense + Commercial Sensing: Quantum Information Science, Sensing, and Computation X

Automated Synthesis of Quantum Subcircuits

The quantum computer has become contemporary reality, with the first two-qubit machine of mere decades ago transforming into cloud-accessible devices with tens, hundreds, or--in a few cases--even thousands of qubits. While such hardware is noisy and still relatively small, the increasing number of operable qubits raises another challenge: how to develop the now-sizeable quantum circuits executable on these machines. Preparing circuits manually for specifications of any meaningful size is at best tedious and at worst impossible, creating a need for automation. This article describes an automated quantum-software toolkit for synthesis, compilation, and optimization, which transforms classically-specified, irreversible functions to both technology-independent and technology-dependent quantum circuits. We also describe and analyze the toolkit's application to three situations--quantum read-only memories, quantum random number generators, and quantum oracles--and illustrate the toolkit's start-to-finish features from the input of classical functions to the output of quantum circuits ready-to-run on commercial hardware. Furthermore, we illustrate how the toolkit enables research beyond circuit synthesis, including comparison of synthesis and optimization methods and deeper understanding of even well-studied quantum algorithms. As quantum hardware continues to develop, such quantum circuit toolkits will play a critical role in realizing its potential.Comment: 49 pages, 25 figures, 20 table

Sensitivity of Yeast Strains with Long G-Tails to Levels of Telomere-Bound Telomerase

The Saccharomyces cerevisiae Pif1p helicase is a negative regulator of telomere length that acts by removing telomerase from chromosome ends. The catalytic subunit of yeast telomerase, Est2p, is telomere associated throughout most of the cell cycle, with peaks of association in both G1 phase (when telomerase is not active) and late S/G2 phase (when telomerase is active). The G1 association of Est2p requires a specific interaction between Ku and telomerase RNA. In mutants lacking this interaction, telomeres were longer in the absence of Pif1p than in the presence of wild-type PIF1, indicating that endogenous Pif1p inhibits the active S/G2 form of telomerase. Pif1p abundance was cell cycle regulated, low in G1 and early S phase and peaking late in the cell cycle. Low Pif1p abundance in G1 phase was anaphase-promoting complex dependent. Thus, endogenous Pif1p is unlikely to act on G1 bound Est2p. Overexpression of Pif1p from a non-cell cycle-regulated promoter dramatically reduced viability in five strains with impaired end protection (cdc13–1, yku80Δ, yku70Δ, yku80–1, and yku80–4), all of which have longer single-strand G-tails than wild-type cells. This reduced viability was suppressed by deleting the EXO1 gene, which encodes a nuclease that acts at compromised telomeres, suggesting that the removal of telomerase by Pif1p exposed telomeres to further C-strand degradation. Consistent with this interpretation, depletion of Pif1p, which increases the amount of telomere-bound telomerase, suppressed the temperature sensitivity of yku70Δ and cdc13–1 cells. Furthermore, eliminating the pathway that recruits Est2p to telomeres in G1 phase in a cdc13–1 strain also reduced viability. These data suggest that wild-type levels of telomere-bound telomerase are critical for the viability of strains whose telomeres are already susceptible to degradation

A fast radio burst with a low dispersion measure

Fast radio bursts (FRBs) are millisecond pulses of radio emission of seemingly extragalactic origin. More than 50 FRBs have now been detected, with only one seen to repeat. Here we present a new FRB discovery, FRB 110214, which was detected in the high latitude portion of the High Time Resolution Universe South survey at the Parkes telescope. FRB 110214 has one of the lowest dispersion measures of any known FRB (DM = 168.9$\pm$0.5 pc cm$^{-3}$), and was detected in two beams of the Parkes multi-beam receiver. A triangulation of the burst origin on the sky identified three possible regions in the beam pattern where it may have originated, all in sidelobes of the primary detection beam. Depending on the true location of the burst the intrinsic fluence is estimated to fall in the range of 50 -- 2000 Jy ms, making FRB 110214 one of the highest-fluence FRBs detected with the Parkes telescope. No repeating pulses were seen in almost 100 hours of follow-up observations with the Parkes telescope down to a limiting fluence of 0.3 Jy ms for a 2-ms pulse. Similar low-DM, ultra-bright FRBs may be detected in telescope sidelobes in the future, making careful modeling of multi-beam instrument beam patterns of utmost importance for upcoming FRB surveys.Comment: 8 pages, 3 figures, accepted for publication in MNRA

Comparison of pretreatment characteristics and treatment outcomes for alcohol-, cocaine-, and multisubstance-dependent patients.

We investigated whether pretreatment characteristics and measures of outcome differed for alcohol-, cocaine-, and multisubstance-dependent patients receiving outpatient substance abuse treatment. One hundred and forty substance dependent individuals (32 alcohol, 76 cocaine, and 32 multisubstance) enrolled in a 12-week outpatient treatment program were compared across measures of addiction severity, personality, and treatment-readiness at admission. In-treatment, end-of-treatment and 9-month follow-up assessments of treatment outcome were then compared across the three groups. Outcome measures included reduction in problem severity, abstinence, retention, number of sessions attended, dropout, and counselor and patient ratings of treatment benefit. At admission, the multisubstance group had a higher proportion of positive urines, reported more severe drug, alcohol and psychiatric problems, and displayed higher impulsivity and anxiety scores than one or both of the other groups. However, multisubstance patients were more treatment ready in terms of adopting a total abstinence orientation than alcohol or cocaine patients. While a significant reduction in symptoms occurred for the total sample during treatment as well as at follow-up, comparisons of outcomes did not consistently favor any particular group. The three groups had equivalent improvements in eleven of fourteen during-treatment and five of seven follow-up measures. Despite pretreatment differences, in severity and treatment-readiness, outcomes were more similar than different for alcohol-, cocaine-, and multisubstance-dependent patients. Clinicians should be cautious about forecasting treatment-outcomes for addicted patients based on their primary substances of abuse