Authorship Attribution Using a Neural Network Language Model

In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2:5% reduction in perplexity and increases author classification accuracy by 3:43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at https://github.com/zge/authorship-attribution.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16

Approximating the partition function of the ferromagnetic Potts model

We provide evidence that it is computationally difficult to approximate the partition function of the ferromagnetic q-state Potts model when q>2. Specifically we show that the partition function is hard for the complexity class #RHPi_1 under approximation-preserving reducibility. Thus, it is as hard to approximate the partition function as it is to find approximate solutions to a wide range of counting problems, including that of determining the number of independent sets in a bipartite graph. Our proof exploits the first order phase transition of the "random cluster" model, which is a probability distribution on graphs that is closely related to the q-state Potts model.Comment: Minor correction

Vanadium(v) phenolate complexes for ring opening homo- and co-polymerisation of ε-caprolactone, L-lactide and rac-lactide

The vanadyl complexes [VO(OtBu)L¹ ] (1) and {[VO(OiPr)]₂ (μ-p-L²ᵖ)} (2) {[VO(OR)]₂ (μ-p-L²ᵐ )} (R = iPr 3, tBu 4) have been prepared from [VO(OR)₃ ] (R = nPr, iPr or tBu) and the respective phenol, namely 2,2′-ethylidenebis(4,6-di-tert-butylphenol) (L¹ H₂ ) or α,α,α′,α′-tetra(3,5-di-tert-butyl-2-hydroxyphenyl–p/m-)xylene-para-tetraphenol (L2p/mH₄). For comparative studies, the known complexes [VO(μ-OnPr)L¹]₂ (I), [VOL³ ]₂ (II) (L³H₃ = 2,6-bis(3,5-di-tert-butyl-2-hydroxybenzyl)-4-tert-butylphenol) were prepared. An imido complex {[VCl(Np-tolyl)(NCMe)]₂(μ-p-L²ᵖ)} (5) has been prepared following work-up from [V(Np-tolyl)Cl₃ ], L²ᵖH₄ and Et₃ N. The molecular structures of complexes 1–5 are reported. Complexes 1–5 and I and II have been screened for their ability to ring open polymerise ε-caprolactone, L-lactide or rac-lactide with and without solvent present. The co-polymerization of ε-caprolactone with L-lactide or rac-lactide afforded co-polymers with low lactide content; the reverse addition was ineffective

Theoretical Study of the Pyrolysis of Methyltrichlorosilane in the Gas Phase. 1. Thermodynamics

Structures and energies of the gas-phase species produced during and after the various unimolecular decomposition reactions of methyltrichlorosilane (MTS) with the presence of H2 carrier gas were determined using second-order perturbation theory (MP2). Single point energies were obtained using singles + doubles coupled cluster theory, augmented by perturbative triples, CCSD(T). Partition functions were obtained using the harmonic oscillator-rigid rotor approximation. A 114-reaction mechanism is proposed to account for the gas-phase chemistry of MTS decompositions. Reaction enthalpies, entropies, and Gibbs free energies for these reactions were obtained at 11 temperatures ranging from 0 to 2000 K including room temperature and typical chemical vapor deposition (CVD) temperatures. Calculated and experimental thermodynamic properties such as heat capacities and entropies of various species and reaction enthalpies are compared, and theory is found to provide good agreement with experiment