Monte Carlo Hamiltonian: the Linear Potentials

We further study the validity of the Monte Carlo Hamiltonian method. The advantage of the method, in comparison with the standard Monte Carlo Lagrangian approach, is its capability to study the excited states. We consider two quantum mechanical models: a symmetric one $V(x) = |x|/2$; and an asymmetric one $V(x)=\infty$, for $x < 0$ and $V(x)=x$, for $x \ge 0$. The results for the spectrum, wave functions and thermodynamical observables are in agreement with the analytical or Runge-Kutta calculations.Comment: Latex file, 8 figure

Monte Carlo Hamiltonian

We suggest how to construct an effective low energy Hamiltonian via Monte Carlo starting from a given action. We test it by computing thermodynamical observables like average energy and specific heat for simple quantum systems.Comment: Contribution to Lattice'99 (Theoretical developments) Text (LaTeX file) + 2 figures (ps files

The Design and Construction of K11: A Novel α-Helical Antimicrobial Peptide

Amphipathic α-helical antimicrobial peptides comprise a class of broad-spectrum agents that are used against pathogens. We designed a series of antimicrobial peptides, CP-P (KWKSFIKKLTSKFLHLAKKF) and its derivatives, and determined their minimum inhibitory concentrations (MICs) against Pseudomonas aeruginosa, their minimum hemolytic concentrations (MHCs) for human erythrocytes, and the Therapeutic Index (MHC/MIC ratio). We selected the derivative peptide K11, which had the highest therapeutic index (320) among the tested peptides, to determine the MICs against Gram-positive and Gram-negative bacteria and 22 clinical isolates including Acinetobacter baumannii, methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Klebsiella pneumonia. K11 exhibited low MICs (less than 10 μg/mL) and broad-spectrum antimicrobial activity, especially against clinically isolated drug-resistant pathogens. Therefore, these results indicate that K11 is a promising candidate antimicrobial peptide for further studies

Poly[diaqua­bis(μ3-1H-benzimidazole-5,6-dicarboxyl­ato-κ4 N 3:O 5,O 5′:O 6)bis­(μ2-1H,3H-benzimidazolium-5,6-dicarboxyl­ato-κ3 O 5,O 5′:O 6)digadolinium(III)]

In the title complex, [Gd2(C9H4N2O4)2(C9H5N2O4)2(H2O)2]n, two of the benzimidazole-5,6-dicarboxyl­ate ligands are pro­ton­ated at the imidazole groups. Each GdIII ion is coordinated by six O atoms and one N atom from five ligands and one water mol­ecule, displaying a distorted bicapped trigonal-prismatic geometry. The GdIII ions are linked by the carboxyl­ate groups and imidazole N atoms, forming a layer parallel to (001). These layers are further connected by O—H⋯O and N—H⋯O hydrogen bonds into a three-dimensional supra­molecular network

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability