LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

Synthesis of heterocyclic metal complexes and their evaluation as marine antifoulants

4-Amino-3-thio-1,24-triazolidine (L₁) and 4-amino-5-thio-1,3,4-thiadiazole (L_II) as well as their metal complexes of the general formulae ML_I·2H₂O and ML_IICl (M; Pb(II), Cd(II) and Zn(II)) were prepared. With Sn(II), we obtained Sn(L_I)₂·2H₂O and SnL_IICl, respectively. The structures of the compounds were identified through elemental analysis, and IR and UV spectra measurements, in addition to thermal analysis in case of the metal complexes. The antifouling properties of the compounds were tested by their incorporation into paint formulations which were applied to PVC substrates and tested in water from Alexandria western harbour. When the compounds were added at 17·5% by volume, the coated panels were fouled after 3 months of immersion. The addition of 6·7% by volume of tributyltin oxide to 15·8% of the prepared compounds in one formulation elevated the paint efficiency and prevented fouling for 11 months. Paint containing solely tributyltin oxide at the same concentration was inactive against algae.     

Synthesis of some new 3-(2′-heterocyclicethyl)-2-methyl-3,4-dihydroquinazolin-4-one derivatives as antimicrobial agents

3-(2′-Chloroethyl)-2-methyl-3,4-dihydroquinazolin-4-one was reacted with acetylacetone, ethyl acetoacetate and diethylmalonate in the presence of sodium ethoxide to afford the alkylation products IV, V and VI , Compounds IV, V and VI were reacted with hydrazine hydrate, phenylhydrazine, hydroxylamine hydrochloride, urea and thiourea to yield 3-(2′-heterocyclicethyl)-2-methyl-3,4-dihydroquinazolin-4-one derivatives VII-XV . The structures of the synthesized compounds were elucidated by elemental analyses and spectroscopic (IR and XH-NMR) analyses. The prepared compounds were tested for their antimicrobial activities in comparison with tetracycline as a reference compound.     

Co-evolution as a computational and cognitive model of design

Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space. The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives. Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification. This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions. The study shows that co-evolutionary design is a good cognitive model of design and highlights the similarities and differences between the computational model and the cognitive model. The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process. Electronic Publication     

Pancreaticoduodenectomy for selected periampullary neuroendocrine tumors: fifty patients

BACKGROUND: Most resectable pancreatic or peripancreatic neuroendocrine tumors are treated by enucleation or distal pancreatectomy. A minority of tumors may require pancreaticoduodenectomy for complete tumor excision because of their large size, location, or lymph node involvement. METHODS: This study reviews the management of 50 patients treated by pancreaticoduodenectomy for periampullary neuroendocrine tumors between 1962 and 1996 at a single institution. RESULTS: There were 30 men and 20 women with a mean age of 52 +/- 2 years. Functional tumors were resected in 17 patients: insulinoma, seven tumors; gastrinoma, eight tumors; vipoma, one tumor; and glucagonoma, one tumor. Tumors were classified as malignant in 29 patients and benign in 21. The median intraoperative blood loss was 800 ml, and the median number of units of blood transfused was zero. The postoperative length of stay was 20 +/- 2 days. Postoperative morbidity included 11 patients (24%) with a pancreatic fistula and four patients (8%) with a biliary fistula. There was one in-hospital death (2%), in 1967. The actuarial survival rates at 2, 5, and 7 years are 81%, 73%, and 65%, respectively. Patients with benign tumors had a significantly improved 5-year survival rate (94%) compared with those with malignant tumors (61%; p = 0.03). CONCLUSIONS: Selected patients with periampullary neuroendocrine tumors can be managed successfully by pancreaticoduodenectomy, with low mortality and acceptable morbidity rates.     

Arylidene polymers. XVIII. Synthesis and thermal behavior of organometallic arylidene polyesters containing ferrocene derivatives in the main chain

A new interesting category of organometallic polyesters based on diarylidenecycloalkanones containing ferrocene derivatives in the polymer main chain has been prepared by interfacial polycondensation of 1,1′-dichlorocarbonyl ferrocene or 1,1′-dichlorocarbonyl-4,4′-diiodoferrocene with 2,5-bis(p-hydroxybenzylidene)cyclopentanone, 2,5-divanillylidenecyclopentanone, 2,6-bis(p-hydroxybenzylidene)cyclohexanone, 2,6-divanillylidenecyclohexanone, and 2,7-bis(p-hydroxybenzylidene) cycloheptanone. The resulting polyesters were characterized by elemental analyses, infrared spectroscopy, solubility, and viscometry measurements. The thermal behavior of the synthesized polymers was evaluated by thermal gravimetric analysis and correlated with their structures. The crystallinity of all polymers were examined by x-ray diffraction analysis. Moreover, the electrical conductivity of a selected example of polymer was investigated above the temperature range (300–500 K) and showed that it followed an Arrhenius-type equation with activation energy 2.09 eV. © 1993 John Wiley & Sons, Inc.     

Random arithmetic formulas can be reconstructed efficiently

Informally stated, we present here a randomized algorithm that given black-box access to the polynomial f computed by an unknown/hidden arithmetic formula ? reconstructs, on the average, an equivalent or smaller formula \({\hat{\phi}}\) in time polynomial in the size of its output \({\hat{\phi}}\) . Specifically, we consider arithmetic formulas wherein the underlying tree is a complete binary tree, the leaf nodes are labeled by affine forms (i.e., degree one polynomials) over the input variables and where the internal nodes consist of alternating layers of addition and multiplication gates. We call these alternating normal form (ANF) formulas. If a polynomial f can be computed by an arithmetic formula μ of size s, it can also be computed by an ANF formula ?, possibly of slightly larger size s ^O(1). Our algorithm gets as input black-box access to the output polynomial f (i.e., for any point x in the domain, it can query the black box and obtain f(x) in one step) of a random ANF formula ? of size s (wherein the coefficients of the affine forms in the leaf nodes of ? are chosen independently and uniformly at random from a large enough subset of the underlying field). With high probability (over the choice of coefficients in the leaf nodes), the algorithm efficiently (i.e., in time s ^O(1)) computes an ANF formula \({\hat{\phi}}\) of size s computing f. This then is the strongest model of arithmetic computation for which a reconstruction algorithm is presently known, albeit efficient in a distributional sense rather than in the worst case.     

Flow measurements in sewers based on image analysis: automatic flow velocity algorithm

Discharges of combined sewer overflows (CSOs) and stormwater are recognized as an important source of environmental contamination. However, the harsh sewer environment and particular hydraulic conditions during rain events reduce the reliability of traditional flow measurement probes. An in situ system for sewer water flow monitoring based on video images was evaluated. Algorithms to determine water velocities were developed based on image-processing techniques. The image-based water velocity algorithm identifies surface features and measures their positions with respect to real world coordinates. A web-based user interface and a three-tier system architecture enable remote configuration of the cameras and the image-processing algorithms in order to calculate automatically flow velocity on-line. Results of investigations conducted in a CSO are presented. The system was found to measure reliably water velocities, thereby providing the means to understand particular hydraulic behaviors.     

A branch and bound algorithm for extracting smallest minimal unsatisfiable subformulas

Explaining the causes of infeasibility of Boolean formulas has practical applications in numerous fields, such as artificial intelligence (repairing inconsistent knowledge bases), formal verification (abstraction refinement and unbounded model checking), and electronic design (diagnosing and correcting infeasibility). Minimal unsatisfiable subformulas (MUSes) provide useful insights into the causes of infeasibility. An unsatisfiable formula often has many MUSes. Based on the application domain, however, MUSes with specific properties might be of interest. In this paper, we tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. An SMUS provides a succinct explanation of infeasibility and is valuable for applications that are heavily affected by the size of the explanation. We present (1) a baseline algorithm for finding an SMUS, founded on earlier work for finding all MUSes, and (2) a new branch-and-bound algorithm called Digger that computes a strong lower bound on the size of an SMUS and splits the problem into more tractable subformulas in a recursive search tree. Using two benchmark suites, we experimentally compare Digger to the baseline algorithm and to an existing incomplete genetic algorithm approach. Digger is shown to be faster in nearly all cases. It is also able to solve far more instances within a given runtime limit than either of the other approaches.