Biguanide-, imine-, and guanidine-based networks as catalysts for transesterification of vegetable oil

Polycationic systems based on poly(hexamethylene biguanide) (PHMBG), branched polyethyleneimine (PEI) and poly(N-vinylguanidine) (PVG) have been evaluated as heterogeneous catalysts for the transesterification of sunflower oil by methanol. Insoluble networks are synthesized via cross-linking of PHMBG by either 4,4′-methylenebis(N,N-diglycidylaniline) or polyisocyanate prepolymer, PEI with sebacoyl chloride, and PVG with 1,4-butanediol diglycidyl ether. PHMBG and its cross-linked networks appeared to be remarkably efficient catalysts, enabling 80–100% triglyceride conversion within 0.5 h at 70 °C. PEI-based networks catalyzed triglyceride transesterification with rates 8- to 12-fold slower than their PHMBG-based counterparts. The PVG-based networks, which were devoid of hydrophobic moieties, appeared to be inefficient catalysts due to limited accessibility of the basic guanidine groups to reactants. The PHMBG networks were shown to be recyclable by a simple centrifugal filtration. After 15 cycles of recovery and reuse, only 10–15% decline in performance was observed.     

Optimal control of nonlinear systems to given orbits

Using optimal control techniques we derive and demonstrate the use of an explicit single-step control method for directing a nonlinear system to a target orbit and keeping it there. We require that control values remain near the uncontrolled settings. The full nonlinearity of the problem in state space variables is retained. The “one-step” of the control is typically a composition of known or learned maps over (a) the time required to learn the state, (b) the time to compute the control and (c) the time to apply the control. No special targeting is required, yet the time to control is quite rapid. Working with the dynamics of a well-studied nonlinear electrical circuit, we show how this method works efficiently and accurately in two situations: when the known circuit equations are used, and when control is performed only on a Poincaré section of the reconstructed phase space. In each case, because the control rule is known analytically, the control strategy is computationally efficient while retaining high accuracy. The target locations on the selected target trajectory at each control stage are determined dynamically by the initial conditions and the system dynamics, and the target trajectory is an approximation to an unstable periodic orbit of the uncontrolled system. A linear stability analysis shows that dissipation in the dynamical system is essential for reaching a controllable state.     

Outlier Detection under Interval Uncertainty: Algorithmic Solvability and Computational Complexity

In many application areas,it is important to detect outliers. The traditional engineering approach to outlier detection is that we start with some normal values x₁, ...,x_n, compute the sample average E, the sample standard variation , and then mark a value x as an outlier if x is outside the k₀-sigma interval [E – k₀ , E + k₀ ] (for some pre-selected parameter k₀).In real life,we often have only interval ranges [ ] for the normal values x₁, ...,x_n. In this case,we only have intervals of possible values for the bounds and . We can therefore identify outliers as values that are outside all k₀-sigma intervals.Once we identify a value as an outlier for a fixed k₀, it is also desirable to find out to what degree this value is an outlier, i.e., what is the largest value k₀ for which this value is an outlier.In this paper,we analyze the computational complexity of these outlier detection problems, provide efficient algorithms that solve some of these problems (under reasonable conditions), and list related open problems.     

Continuous input nonlocal games

We present a family of nonlocal games in which the inputs the players receive are continuous. We study three representative members of the family. For the first two a team sharing quantum correlations (entanglement) has an advantage over any team restricted to classical correlations. We conjecture that this is true for the third member of the family as well.     

Bayesian hierarchical model with MCMC for optical cable flame retardancy tests

We report a model and method for calculating the probability of an optical fiber cable passing a flame retardancy test (FRT). The method uses a Bayesian approach that accounts for variations in the experimental conditions between tests. We show that adding a hierarchy to the empirical model shrinks the posterior distributions and discuss the applicability of the hierarchical model to various test environments. The model uses the average smoke parameter as the predictor and can be extended to other test responses such as the peak smoke, flame spread, or toxicity.     

Representation before computation

My main objective is to point out a fundamental weakness in the conventional conception of computation and suggest a promising way out. This weakness is directly related to a gross underestimation of the role of object representation in a computational model, hence confining such models to an unrealistic (input) environment, which, in turn, leads to “unnatural” computational models. This lack of appreciation of the role of structural object representation has been inherited from logic and partly from mathematics, where, in the latter, the centuries-old tradition is to represent objects as unstructured “points”. I also discuss why the appropriate fundamental reorientation in the conception of computational models will bring the resulting study of computation closer to the “natural” computational constrains. An example of the pertinent, class-oriented, representational formalism developed by our group over many years—Evolving Transformation System (ETS)—is briefly outlined here, and several related general lines of research are suggested.     

Lattice Distortions and Charge Carriers in Cuprates

A phenomenological model with itinerant bands and local states trapped by thelattice on the Cu-sites, is discussed to describe global features ofcuprates. Relative energy positions of localized and itinerant states beingtuned (thermodynamically or by doping), the system must undergo first-orderMott metal-insulator transition. Decreasing the local level (from themetallic end of a stoichiometric compound), charge separation instabilityoccurs first before the Mott transition. Crossing and hybridization betweenlocal (flat) and itinerant bands introduce a structure in density of stateswhich may account for pseudogap features in cuprates. Modelresults in polaronic lattice effects and is rich enough to serve as aphenomenology of cuprates.     

Structured learning with constrained conditional models

Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where an expressive dependency structure among these can influence, or even dictate, what assignments are possible. Commonly used models typically ignore expressive dependencies since the traditional way of incorporating non-local dependencies is inefficient and hence leads to expensive training and inference. The contribution of this paper is two-fold. First, this paper presents Constrained Conditional Models (CCMs), a?framework that augments linear models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. The paper develops, analyzes and compares novel algorithms for CCMs based on Hidden Markov Models and Structured Perceptron. The proposed CCM framework is also compared to task-tailored models, such as semi-CRFs. Second, we propose CoDL, a?constraint-driven learning algorithm, which makes use of constraints to guide semi-supervised learning. We provide theoretical justification for CoDL along with empirical results which show the advantage of using declarative constraints in the context of semi-supervised training of probabilistic models.     

An unsupervised data projection that preserves the cluster structure

In this paper we propose a new unsupervised dimensionality reduction algorithm that looks for a projection that optimally preserves the clustering data structure of the original space. Formally we attempt to find a projection that maximizes the mutual information between data points and clusters in the projected space. In order to compute the mutual information, we neither assume the data are given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a linear projection and a clustering that maximizes the estimated mutual information between the projected data points and the clusters. The improved performance is demonstrated on both synthetic and real world examples.