Morphosemantic Categories in Chinese: An Interim Report

This paper applies the notion of the homonerae - a sub-morphemic semantic unit - to the morphosemantic analysis of Cantonese. A number of major morphosemantic categories and subcategories in Chinese are identified through analysis of monomorphemic lexical items. The morphosemantic approach adopted is related to the 'shengxun' branch of traditional Chinese linguistics

Single-Shot Visualization Of Evolving Laser- Or Beam-Driven Plasma Wakefield Accelerators

We introduce Frequency-Domain Tomography (FDT) for visualizing sub-ps evolution of light-speed refractive index structures in a single shot. As a prototype demonstration, we produce single-shot tomographic movies of self-focusing, filamenting laser pulses propagating in a transparent Kerr medium. We then discuss how to adapt FDT to visualize evolving laser-or beam-driven plasma wakefields of current interest to the advanced accelerator community. For short (L similar to 1 cm), dense (n(e) similar to 10(19) cm(-3)) plasmas, the key challenge is broadening probe bandwidth sufficiently to resolve plasma-wavelength-size structures. For long (L similar to 10 to 100 cm), tenuous (n(e) similar to 10(17) cm(-3)) plasmas, probe diffraction from the evolving wake becomes the key challenge. We propose and analyze solutions to these challenges.Physic

Structure of the Partition Function and Transfer Matrices for the Potts Model in a Magnetic Field on Lattice Strips

We determine the general structure of the partition function of the $q$-state Potts model in an external magnetic field, $Z(G,q,v,w)$ for arbitrary $q$, temperature variable $v$, and magnetic field variable $w$, on cyclic, M\"obius, and free strip graphs $G$ of the square (sq), triangular (tri), and honeycomb (hc) lattices with width $L_y$ and arbitrarily great length $L_x$. For the cyclic case we prove that the partition function has the form $Z(\Lambda,L_y \times L_x,q,v,w)=\sum_{d=0}^{L_y} \tilde c^{(d)} Tr[(T_{Z,\Lambda,L_y,d})^m]$, where $\Lambda$ denotes the lattice type, $\tilde c^{(d)}$ are specified polynomials of degree $d$ in $q$, $T_{Z,\Lambda,L_y,d}$ is the corresponding transfer matrix, and $m=L_x$ ($L_x/2$) for $\Lambda=sq, tri (hc)$, respectively. An analogous formula is given for M\"obius strips, while only $T_{Z,\Lambda,L_y,d=0}$ appears for free strips. We exhibit a method for calculating $T_{Z,\Lambda,L_y,d}$ for arbitrary $L_y$ and give illustrative examples. Explicit results for arbitrary $L_y$ are presented for $T_{Z,\Lambda,L_y,d}$ with $d=L_y$ and $d=L_y-1$. We find very simple formulas for the determinant $det(T_{Z,\Lambda,L_y,d})$. We also give results for self-dual cyclic strips of the square lattice.Comment: Reference added to a relevant paper by F. Y. W

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Viscoelastic response of neural cells governed by the deposition of amyloid-ß peptides (Aß)

published_or_final_versio

Measurement of the Dynamical Structure Factor of a 1D Interacting Fermi Gas

We present measurements of the dynamical structure factor $S(q,\omega)$ of an interacting one-dimensional (1D) Fermi gas for small excitation energies. We use the two lowest hyperfine levels of the $^6$Li atom to form a pseudo-spin-1/2 system whose s-wave interactions are tunable via a Feshbach resonance. The atoms are confined to 1D by a two-dimensional optical lattice. Bragg spectroscopy is used to measure a response of the gas to density ("charge") mode excitations at a momentum $q$ and frequency $\omega$. The spectrum is obtained by varying $\omega$, while the angle between two laser beams determines $q$, which is fixed to be less than the Fermi momentum $k_\textrm{F}$. The measurements agree well with Tomonaga-Luttinger theory