Extending scientific computing system with structural quantum programming capabilities

We present a basic high-level structures used for developing quantum programming languages. The presented structures are commonly used in many existing quantum programming languages and we use quantum pseudo-code based on QCL quantum programming language to describe them. We also present the implementation of introduced structures in GNU Octave language for scientific computing. Procedures used in the implementation are available as a package quantum-octave, providing a library of functions, which facilitates the simulation of quantum computing. This package allows also to incorporate high-level programming concepts into the simulation in GNU Octave and Matlab. As such it connects features unique for high-level quantum programming languages, with the full palette of efficient computational routines commonly available in modern scientific computing systems. To present the major features of the described package we provide the implementation of selected quantum algorithms. We also show how quantum errors can be taken into account during the simulation of quantum algorithms using quantum-octave package. This is possible thanks to the ability to operate on density matrices

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

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

Golanudiplosis japonicus - a new midge attacking comstock mealybug in Japan (Diptera: Itonididae).

Die Mücke, die in Japan die Eier der Mehlwanze Pseudococcus comstocki befällt, wird als neue Gattung Golanudiplosis mit der typischen Art japonicus beschrieben. Sowohl Männchen wie Weibchen werden deskripiert, und ihre Stellung im System wird erörtert.Nomenklatorische Handlungenjaponicus Grover &amp; Prasad, 1968 (Golanudiplosis), spec. n.Golanudiplosis Grover &amp; Prasad, 1968 (Itonididae), gen. n.The midge found attacking eggs of the comstocki mealybug, Pseudococcus comstocki, in Japan is described as a new genus Golanudiplosis with japonicus as its type species. Both male and female are described in detail and their systematic position is discussed. Nomenclatural Actsjaponicus Grover &amp; Prasad, 1968 (Golanudiplosis), spec. n.Golanudiplosis Grover &amp; Prasad, 1968 (Itonididae), gen. n

Emergence of supersymmetry on the surface of three dimensional topological insulators

We propose two possible experimental realizations of a 2+1 dimensional spacetime supersymmetry at a quantum critical point on the surface of three dimensional topological insulators. The quantum critical point between the semi-metallic state with one Dirac fermion and the s-wave superconducting state on the surface is described by a supersymmetric conformal field theory within $\epsilon$-expansion. We predict the exact voltage dependence of the differential conductance at the supersymmetric critical point.Comment: 8 pages, 2 figures; published versio

Water balance complexities in ephemeral catchments with different land uses: Insights from monitoring and distributed hydrologic modeling

Although ephemeral catchments are widespread in arid and semiarid climates, the relationship of their water balance with climate, geology, topography, and land cover is poorly known. Here we use 4 years (2011–2014) of rainfall, streamflow, and groundwater level measurements to estimate the water balance components in two adjacent ephemeral catchments in south-eastern Australia, with one catchment planted with young eucalypts and the other dedicated to grazing pasture. To corroborate the interpretation of the observations, the physically based hydrological model CATHY was calibrated and validated against the data in the two catchments. The estimated water balances showed that despite a significant decline in groundwater level and greater evapotranspiration in the eucalypt catchment (104–119% of rainfall) compared with the pasture catchment (95–104% of rainfall), streamflow consistently accounted for 1–4% of rainfall in both catchments for the entire study period. Streamflow in the two catchments was mostly driven by the rainfall regime, particularly rainfall frequency (i.e., the number of rain days per year), while the downslope orientation of the plantation furrows also promoted runoff. With minimum calibration, the model was able to adequately reproduce the periods of flow in both catchments in all years. Although streamflow and groundwater levels were better reproduced in the pasture than in the plantation, model-computed water balance terms confirmed the estimates from the observations in both catchments. Overall, the interplay of climate, topography, and geology seems to overshadow the effect of land use in the study catchments, indicating that the management of ephemeral catchments remains highly challenging