SINDA/SINFLO computer routine, volume 1, revision A

The SINFLO package was developed to modify the SINDA preprocessor to accept and store the input data for fluid flow systems analysis and adding the FLOSOL user subroutine to perform the flow solution. This reduced and simplified the user input required for analysis of flow problems. A temperature calculation method, the flow-hybrid method which was developed in previous VSD thermal simulator routines, was incorporated for calculating fluid temperatures. The calculation method accuracy was improved by using fluid enthalpy rather than specific heat for the convective term of the fluid temperature equation. Subroutines and data input requirements are described along with user subroutines, flow data storage, and usage of the plot program

Approximation Algorithms for the Max-Buying Problem with Limited Supply

We consider the Max-Buying Problem with Limited Supply, in which there are $n$ items, with $C_i$ copies of each item $i$, and $m$ bidders such that every bidder $b$ has valuation $v_{ib}$ for item $i$. The goal is to find a pricing $p$ and an allocation of items to bidders that maximizes the profit, where every item is allocated to at most $C_i$ bidders, every bidder receives at most one item and if a bidder $b$ receives item $i$ then $p_i \leq v_{ib}$. Briest and Krysta presented a 2-approximation for this problem and Aggarwal et al. presented a 4-approximation for the Price Ladder variant where the pricing must be non-increasing (that is, $p_1 \geq p_2 \geq \cdots \geq p_n$). We present an $e/(e-1)$-approximation for the Max-Buying Problem with Limited Supply and, for every $\varepsilon > 0$, a $(2+\varepsilon)$-approximation for the Price Ladder variant

The Provable Virtue of Laziness in Motion Planning

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case

Discrete Self-Similarity in Type-II Strong Explosions

We present new solutions to the strong explosion problem in a non-power law density profile. The unperturbed self-similar solutions discovered by Waxman & Shvarts describe strong Newtonian shocks propagating into a cold gas with a density profile falling off as $r^{-\omega}$, where $\omega>3$ (Type-II solutions). The perturbations we consider are spherically symmetric and log-periodic with respect to the radius. While the unperturbed solutions are continuously self-similar, the log-periodicity of the density perturbations leads to a discrete self-similarity of the perturbations, i.e. the solution repeats itself up to a scaling at discrete time intervals. We discuss these solutions and verify them against numerical integrations of the time dependent hydrodynamic equations. Finally we show that this method can be generalized to treat any small, spherically symmetric density perturbation by employing Fourier decomposition

An O(n^3)-Time Algorithm for Tree Edit Distance

The {\em edit distance} between two ordered trees with vertex labels is the minimum cost of transforming one tree into the other by a sequence of elementary operations consisting of deleting and relabeling existing nodes, as well as inserting new nodes. In this paper, we present a worst-case $O(n^3)$-time algorithm for this problem, improving the previous best $O(n^3\log n)$-time algorithm~\cite{Klein}. Our result requires a novel adaptive strategy for deciding how a dynamic program divides into subproblems (which is interesting in its own right), together with a deeper understanding of the previous algorithms for the problem. We also prove the optimality of our algorithm among the family of \emph{decomposition strategy} algorithms--which also includes the previous fastest algorithms--by tightening the known lower bound of $\Omega(n^2\log^2 n)$~\cite{Touzet} to $\Omega(n^3)$, matching our algorithm's running time. Furthermore, we obtain matching upper and lower bounds of $\Theta(n m^2 (1 + \log \frac{n}{m}))$ when the two trees have different sizes $m$ and~$n$, where $m < n$.Comment: 10 pages, 5 figures, 5 .tex files where TED.tex is the main on

Nonlinear Induction Detection of Electron Spin Resonance

We present a new approach to the induction detection of electron spin resonance (ESR) signals exploiting the nonlinear properties of a superconducting resonator. Our experiments employ a yttrium barium copper oxide (YBCO) superconducting stripline microwave (MW) resonator integrated with a microbridge. A strong nonlinear response of the resonator is thermally activated in the microbridge when exceeding a threshold in the injected MW power. The responsivity factor characterizing the ESR-induced change in the system's output signal is about 100 times larger when operating the resonator near the instability threshold, compared to the value obtained in the linear regime of operation. Preliminary experimental results, together with a theoretical model of this phenomenon are presented. Under appropriate conditions nonlinear induction detection of ESR can potentially improve upon the current capabilities of conventional linear induction detection ESR

An Information Maximization Approach to Overcomplete and Recurrent Representations

The principle of maximizing mutual information is applied to learning overcomplete and recurrent representations. The underlying model consists of a network of input units driving a larger number of output units with recurrent interactions. In the limit of zero noise, the network is deterministic and the mutual information can be related to the entropy of the output units. Maximizing this entropy with respect to both the feedforward connections as well as the recurrent interactions results in simple learning rules for both sets of parameters. The conventional independent components (ICA) learning algorithm can be recovered as a special case where there is an equal number of output units and no recurrent connections. The application of these new learning rules is illustrated on a simple two-dimensional input example

Thermal and flow analysis subroutines for the SINDA-version 9 computer routine

Fluid flow analysis, special thermal analysis and input/output capabilities of the MOTAR routine were incorporated into the SINDA routine. All the capabilities were added in the form of user subroutines so that they may be added to different versions of SINDA with a minimum of programmer effort. Two modifications were made to the existing subroutines of SINDA/8 to incorporate the above subroutines. These were: (1) A modification to the preprocessor to permit actual values of array numbers, conductor numbers, node numbers or constant numbers supplied as array data to be converted to relative numbers. (2) Modifications to execution subroutine CNFAST to make it compatible with the radiant interchange user subroutine, RADIR. This modified version of SINDA has been designated SINDA/version 9. A detailed discussion of the methods used for the capabilities added is presented. The modifications for the SINDA subroutines are described, as well as user subroutines. All subroutines added or modified are listed

SINDA/SINFLO computer routine, volume 2, revision A

Listings of subroutines which were added and modified during the development of SINDA/SINFLO are presented in alphabetical order. For Volume 1, see

The Value of Native Plants and Local Production in an Era of Global Agriculture

For addressing potential food shortages, a fundamental tradeoff exists between investing more resources to increasing productivity of existing crops, as opposed to increasing crop diversity by incorporating more species. We explore ways to use local plants as food resources and the potential to promote food diversity and agricultural resilience. We discuss how use of local plants and the practice of local agriculture can contribute to ongoing adaptability in times of global change. Most food crops are now produced, transported, and consumed long distances from their homelands of origin. At the same time, research and practices are directed primarily at improving the productivity of a small number of existing crops that form the cornerstone of a global food economy, rather than to increasing crop diversity. The result is a loss of agro-biodiversity, leading to a food industry that is more susceptible to abiotic and biotic stressors, and more at risk of catastrophic losses. Humans cultivate only about 150 of an estimated 30,000 edible plant species worldwide, with only 30 plant species comprising the vast majority of our diets. To some extent, these practices explain the food disparity among human populations, where nearly 1 billion people suffer insufficient nutrition and 2 billion people are obese or overweight. Commercial uses of new crops and wild plants of local origin have the potential to diversify global food production and better enable local adaptation to the diverse environments humans inhabit. We discuss the advantages, obstacles, and risks of using local plants. We also describe a case study—the missed opportunity to produce pine nuts commercially in the Western United States. We discuss the potential consequences of using local pine nuts rather than importing them overseas. Finally, we provide a list of edible native plants, and synthesize the state of research concerning the potential and challenges in using them for food production. The goal of our synthesis is to support more local food production using native plants in an ecologically sustainable manner