Non-unique factorization of polynomials over residue class rings of the integers

We investigate non-unique factorization of polynomials in Z_{p^n}[x] into irreducibles. As a Noetherian ring whose zero-divisors are contained in the Jacobson radical, Z_{p^n}[x] is atomic. We reduce the question of factoring arbitrary non-zero polynomials into irreducibles to the problem of factoring monic polynomials into monic irreducibles. The multiplicative monoid of monic polynomials of Z_{p^n}[x] is a direct sum of monoids corresponding to irreducible polynomials in Z_p[x], and we show that each of these monoids has infinite elasticity. Moreover, for every positive integer m, there exists in each of these monoids a product of 2 irreducibles that can also be represented as a product of m irreducibles.Comment: 11 page

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape

Scaling and non-Abelian signature in fractional quantum Hall quasiparticle tunneling amplitude

We study the scaling behavior in the tunneling amplitude when quasiparticles tunnel along a straight path between the two edges of a fractional quantum Hall annulus. Such scaling behavior originates from the propagation and tunneling of charged quasielectrons and quasiholes in an effective field analysis. In the limit when the annulus deforms continuously into a quasi-one-dimensional ring, we conjecture the exact functional form of the tunneling amplitude for several cases, which reproduces the numerical results in finite systems exactly. The results for Abelian quasiparticle tunneling is consistent with the scaling anaysis; this allows for the extraction of the conformal dimensions of the quasiparticles. We analyze the scaling behavior of both Abelian and non-Abelian quasiparticles in the Read-Rezayi Z_k-parafermion states. Interestingly, the non-Abelian quasiparticle tunneling amplitudes exhibit nontrivial k-dependent corrections to the scaling exponent.Comment: 16 pages, 4 figure

Spin Waves in Random Spin Chains

We study quantum spin-1/2 Heisenberg ferromagnetic chains with dilute, random antiferromagnetic impurity bonds with modified spin-wave theory. By describing thermal excitations in the language of spin waves, we successfully observe a low-temperature Curie susceptibility due to formation of large spin clusters first predicted by the real-space renormalization-group approach, as well as a crossover to a pure ferromagnetic spin chain behavior at intermediate and high temperatures. We compare our results of the modified spin-wave theory to quantum Monte Carlo simulations.Comment: 3 pages, 3 eps figures, submitted to the 47th Conference on Magnetism and Magnetic Material

A P300-speller based on event-related spectral perturbation (ERSP)

A brain-computer interface (BCI) P300 speller is a novel technique that helps people spell words using the electroencephalography (EEG) without the involvement of muscle activities. However, only time domain ERP features (P300) are used for controlling of the BCI speller. In this paper, we investigated the time-frequency EEG features for the P300-based brain-computer interface speller. A signal preprocessing method integrated ensemble average, principal component analysis, and independent component analysis to remove noise and artifacts in the EEG data. A time-frequency analysis based on wavelet transform was carried out to extract event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) features. Results showed that the proposed signal processing method can effectively extract EEG time-frequency features in the P300 speller, suggesting that ERSP and ITC may be useful for improving the performance of BCI P300 speller. © 2012 IEEE.published_or_final_versio

Bragg spectroscopy of a superfluid Bose-Hubbard gas

Bragg spectroscopy is used to measure excitations of a trapped, quantum-degenerate gas of 87Rb atoms in a 3-dimensional optical lattice. The measurements are carried out over a range of optical lattice depths in the superfluid phase of the Bose-Hubbard model. For fixed wavevector, the resonant frequency of the excitation is found to decrease with increasing lattice depth. A numerical calculation of the resonant frequencies based on Bogoliubov theory shows a less steep rate of decrease than the measurements.Comment: 11 pages, 4 figure