Situating multimodal learning analytics

The digital age has introduced a host of new challenges and opportunities for the learning sciences community. These challenges and opportunities are particularly abundant in multimodal learning analytics (MMLA), a research methodology that aims to extend work from Educational Data Mining (EDM) and Learning Analytics (LA) to multimodal learning environments by treating multimodal data. Recognizing the short-term opportunities and longterm challenges will help develop proof cases and identify grand challenges that will help propel the field forward. To support the field's growth, we use this paper to describe several ways that MMLA can potentially advance learning sciences research and touch upon key challenges that researchers who utilize MMLA have encountered over the past few years

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Amorphization of Vortex Matter and Reentrant Peak Effect in YBa2_2Cu3_3O7−δ_{7-\delta}

The peak effect (PE) has been observed in a twinned crystal of YBa$_2$Cu$_3$O$_{7-\delta}$ for H$\parallel$c in the low field range, close to the zero field superconducting transition temperature (T$_c$(0)) . A sharp depinning transition succeeds the peak temperature T$_p$ of the PE. The PE phenomenon broadens and its internal structure smoothens out as the field is increased or decreased beyond the interval between 250 Oe and 1000 Oe. Moreover, the PE could not be observed above 10 kOe and below 20 Oe. The locus of the T$_p$(H) values shows a reentrant characteristic with a nose like feature located at T$_p$(H)/T$_c$(0)$\approx$0.99 and H$\approx$100 Oe (where the FLL constant a$_0$$\approx$penetration depth $\lambda$). The upper part of the PE curve (0.5 kOe$<$H$<$10 kOe) can be fitted to a melting scenario with the Lindemann number c$_L$$\approx$0.25. The vortex phase diagram near T$_c$(0) determined from the characteristic features of the PE in YBa$_2$Cu$_3$O$_{7-\delta}$(H$\parallel$c) bears close resemblance to that in the 2H-NbSe$_2$ system, in which a reentrant PE had been observed earlier.Comment: 15 pages and 7 figure

Thermo-magnetic history effects in the vortex state of YNi_2B_2C superconductor

The nature of five-quadrant magnetic isotherms for is different from that for in a single crystal of YNi2B2C, pointing towards an anisotropic behaviour of the flux line lattice (FLL). For, a well defined peak effect (PE) and second magnetization peak (SMP) can be observed and the loop is open prior to the PE. However, for, the loop is closed and one can observe only the PE. We have investigated the history dependence of magnetization hysteresis data for by recording minor hysteresis loops. The observed history dependence in across different anomalous regions are rationalized on the basis of su-perheating/supercooling of the vortex matter across the first-order-like phase transition and possible additional effects due to annealing of the disordered vortex bundles to the underlying equilibrium state.Comment: 4 pages, 4 figure

Indications of superconductivity in doped highly oriented pyrolytic graphite

We have observed possible superconductivity using standard resistance vs. temperature techniques in phosphorous ion implanted Highly Oriented Pyrolytic Graphite. The onset appears to be above 100 K and quenching by an applied magnetic field has been observed. The four initial boron implanted samples showed no signs of becoming superconductive whereas all four initial and eight subsequent samples that were implanted with phosphorous showed at least some sign of the existence of small amounts of the possibly superconducting phases. The observed onset temperature is dependent on both the number of electron donors present and the amount of damage done to the graphene sub-layers in the Highly Oriented Pyrolytic Graphite samples. As a result the data appears to suggest that the potential for far higher onset temperatures in un-damaged doped graphite exists.Comment: 7 pages, 1 table, 5 figures, 11 references, Acknowledgments section was correcte

Comparison of Gravitational Wave Detector Network Sky Localization Approximations

Gravitational waves emitted during compact binary coalescences are a promising source for gravitational-wave detector networks. The accuracy with which the location of the source on the sky can be inferred from gravitational wave data is a limiting factor for several potential scientific goals of gravitational-wave astronomy, including multi-messenger observations. Various methods have been used to estimate the ability of a proposed network to localize sources. Here we compare two techniques for predicting the uncertainty of sky localization -- timing triangulation and the Fisher information matrix approximations -- with Bayesian inference on the full, coherent data set. We find that timing triangulation alone tends to over-estimate the uncertainty in sky localization by a median factor of $4$ for a set of signals from non-spinning compact object binaries ranging up to a total mass of $20 M_\odot$, and the over-estimation increases with the mass of the system. We find that average predictions can be brought to better agreement by the inclusion of phase consistency information in timing-triangulation techniques. However, even after corrections, these techniques can yield significantly different results to the full analysis on specific mock signals. Thus, while the approximate techniques may be useful in providing rapid, large scale estimates of network localization capability, the fully coherent Bayesian analysis gives more robust results for individual signals, particularly in the presence of detector noise.Comment: 11 pages, 7 Figure

Nested quantum search and NP-complete problems

A quantum algorithm is known that solves an unstructured search problem in a number of iterations of order $\sqrt{d}$, where $d$ is the dimension of the search space, whereas any classical algorithm necessarily scales as $O(d)$. It is shown here that an improved quantum search algorithm can be devised that exploits the structure of a tree search problem by nesting this standard search algorithm. The number of iterations required to find the solution of an average instance of a constraint satisfaction problem scales as $\sqrt{d^\alpha}$, with a constant $\alpha<1$ depending on the nesting depth and the problem considered. When applying a single nesting level to a problem with constraints of size 2 such as the graph coloring problem, this constant $\alpha$ is estimated to be around 0.62 for average instances of maximum difficulty. This corresponds to a square-root speedup over a classical nested search algorithm, of which our presented algorithm is the quantum counterpart.Comment: 18 pages RevTeX, 3 Postscript figure

Energy and Efficiency of Adiabatic Quantum Search Algorithms

We present the results of a detailed analysis of a general, unstructured adiabatic quantum search of a data base of $N$ items. In particular we examine the effects on the computation time of adding energy to the system. We find that by increasing the lowest eigenvalue of the time dependent Hamiltonian {\it temporarily} to a maximum of $\propto \sqrt{N}$, it is possible to do the calculation in constant time. This leads us to derive the general theorem which provides the adiabatic analogue of the $\sqrt{N}$ bound of conventional quantum searches. The result suggests that the action associated with the oracle term in the time dependent Hamiltonian is a direct measure of the resources required by the adiabatic quantum search.Comment: 6 pages, Revtex, 1 figure. Theorem modified, references and comments added, sections introduced, typos corrected. Version to appear in J. Phys.

Final Assembly and Initial Irradiation of the First Advanced Gas Reactor Fuel Development and Qualification Experiment in the Advanced Test Reactor

The United States Department of Energy’s Advanced Gas Reactor (AGR) Fuel Development and Qualification Program will be irradiating eight separate low enriched uranium (LEU) oxycarbide (UCO) tri-isotropic (TRISO) particle fuel (in compact form) experiments in the Advanced Test Reactor (ATR) located at the Idaho National Laboratory (INL). The ATR has a long history of irradiation testing in support of reactor development and the INL has been designated as the new United States Department of Energy’s lead laboratory for nuclear energy development. The ATR is one of the world’s premiere test reactors for performing long term, high flux, and/or large volume irradiation test programs. These irradiations and fuel development are being accomplished to support development of the next generation reactors in the United States. The AGR fuel experiments will be irradiated over the next ten years to demonstrate and qualify new particle fuel for use in high temperature gas reactors. The goals of the irradiation experiments are to provide irradiation performance data to support fuel process development, to qualify fuel for normal operating conditions, to support development and validation of fuel performance and fission product transport models and codes, and to provide irradiated fuel and materials for post irradiation examination (PIE) and safety testing.1,2 The experiments, which will each consist of six separate capsules, will be irradiated in an inert sweep gas atmosphere with individual on-line temperature monitoring and control of each capsule. The sweep gas will also have on-line fission product monitoring on its effluent to track performance of the fuel in each individual capsule during irradiation. The final design phase for the first experiment was completed in 2005, and the fabrication and assembly of the first experiment test train (designated AGR-1) as well as the support systems and fission product monitoring system that will monitor and control the experiment during irradiation were completed in 2006. The experiment was inserted in the ATR in December 2006, and will serve as a shakedown test of the multi-capsule experiment design that will be used in the subsequent irradiations as well as a test of the early variants of the fuel produced under this program. The experiment test train as well as the monitoring, control, and data collection systems are discussed