Preparation of Nanostructured Cu 2

Nanostructured Cu-Sn-S powder was prepared by a relatively low-cost, simple, and green solvothermal method. Flower-like Cu2SnS3 nanostructures were successfully synthesized in 50 vol% ethanol water solution at 200 °C for 7.5 h. The structure and photophysical properties of the as-obtained samples were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and UV-Vis diffusion reflectance spectroscopy. Results showed that the cubic and tetragonal Cu2SnS3 was obtained by varying the ethanol contents. The band-gap energy of tetragonal Cu2SnS3 nanocrystals is near the optimum for photovoltaic solar conversion in a single band-gap device

Searching for a DNAzyme version of the leadzyme

The final publication is available at Springer via http://dx.doi.org/10.1007/s00239-015-9702-zThe leadzyme refers to a small ribozyme that cleaves a RNA substrate in the presence of Pb2+. In an optimized form, the enzyme strand contains only two unpaired nucleotides. Most RNA-cleaving DNAzymes are much longer. Two classical Pb2+-dependent DNAzymes, 8–17 and GR5, both contain around 15 nucleotides in the enzyme loop. This is also the size of most RNA-cleaving DNAzymes that use other metal ions for their activity. Such large enzyme loops make spectroscopic characterization difficult and so far no high-resolution structural information is available for active DNAzymes. The goal of this work is to search for DNAzymes with smaller enzyme loops. A simple replacement of the ribonucleotides in the leadzyme by deoxyribonucleotides failed to produce an active enzyme. A Pb2+-dependent in vitro selection combined with deep sequencing was then performed. After sequence alignment and DNA folding, a new DNAzyme named PbE22 was identified, which contains only 5 nucleotides in the enzyme catalytic loop. The biochemical characteristics of PbE22 were compared with those of the leadzyme and the two classical Pb2+-dependent DNAzymes. The rate of PbE22 rises with increase in Pb2+ concentration, being 1.7 h−1 in the presence of 100 μM Pb2+ and reaching 3.5 h−1 at 500 µM Pb2+. The log of PbE22 rate rises linearly in a pH-dependent fashion (20 µM Pb2+) with a slope of 0.74. In addition, many other abundant sequences in the final library were studied. These sequences are quite varied in length and nucleotide composition, but some contain a few conserved nucleotides consistent with the GR5 structure. Interestingly, some sequences are active with Pb2+ but none of them were active with even 50 mM Mg2+, which is reminiscent of the difference between the GR5 and 8–17 DNAzymes.University of Waterloo || Ontario Ministry of Research & Innovation || Natural Sciences and Engineering Research Council |

Delay-induced multiple stochastic resonances on scale-free neuronal networks

We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker respectively to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks.Comment: 7 two-column pages, 5 figures; accepted for publication in Chao

Multi-Fidelity Multi-Armed Bandits Revisited

We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (fidelities) and observation accuracy. We study both the best arm identification with fixed confidence (BAI) and the regret minimization objectives. For BAI, we present (a) a cost complexity lower bound, (b) an algorithmic framework with two alternative fidelity selection procedures, and (c) both procedures' cost complexity upper bounds. From both cost complexity bounds of MF-MAB, one can recover the standard sample complexity bounds of the classic (single-fidelity) MAB. For regret minimization of MF-MAB, we propose a new regret definition, prove its problem-independent regret lower bound $\Omega(K^{1/3}\Lambda^{2/3})$ and problem-dependent lower bound $\Omega(K\log \Lambda)$, where $K$ is the number of arms and $\Lambda$ is the decision budget in terms of cost, and devise an elimination-based algorithm whose worst-cost regret upper bound matches its corresponding lower bound up to some logarithmic terms and, whose problem-dependent bound matches its corresponding lower bound in terms of $\Lambda$

Min-max Submodular Ranking for Multiple Agents

In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents -- thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents.Comment: To appear in AAAI 202

Factorization Bandits for Online Influence Maximization

We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with it, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. And extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.Comment: 11 pages (including SUPPLEMENT

A Carbon Nanotube-based Hundred Watt-level Ka-band Backward Wave Oscillator

Carbon nanotube (CNT) cold-cathodes hold much promise in a variety of millimeter-wave and terahertz vacuum electronic radiation devices due to their inherent near instantaneous temporal turn-on and near-ideal ideal field electron emission performance. Here we report on the development of a CNT cold-cathode Ka -band backward-wave oscillator (BWO). Using a novel beam compression stage, theoretical studies, simulation results, and empirical findings collectively demonstrate that this device affords an unprecedentedly high output power of 230 W at a technologically important operating frequency of 33.65 GHz. The developed magnetic injection electron gun achieves a high emission current of 265.5 mA (emission current density of 188.3 mA/cm 2 ) and a high focused beam current density of 18.5 A/cm 2 , which our studies suggest, is essential to the BWOs high output power

Di-μ-hydroxido-bis­({2,2′-[propane-1,3-diylbis(nitrilo­methyl­idyne)]diphenolato}iron(III)) dimethyl­formamide disolvate

The structure of the title compound, [Fe2(C17H16N2O2)2(OH)2]·2C3H7N, consists of centrosymmetric dimeric units in which crystallographically equivalent FeIII ions are doubly bridged by hydroxide groups. Each FeIII center in the complex has a six-coordinated distorted cis-FeN2O4 octa­hedral geometry