Quantum electric-dipole liquid on a triangular lattice

Geometric frustrations and quantum mechanical fluctuations may prohibit the formation of long-range ordering even at the lowest temperature, and therefore liquid-like ground states could be expected. A good example is the quantum spin liquid in frustrated magnets that represents an exotic phase of matter and is attracting enormous interests. Geometric frustrations and quantum fluctuations can happen beyond magnetic systems. Here we propose that quantum electric-dipole liquids, analogs to quantum spin liquids, could emerge in frustrated dielectrics where antiferroelectrically coupled small electric dipoles reside on a triangular lattice. The quantum paraelectric hexaferrite BaFe12O19, in which small electric dipoles originated from the off-center displacement of Fe3+ in the FeO5 bipyramids constitute a two-dimensional triangular lattice, represents a promising candidate to generate the anticipated electric-dipole liquid. We present a series of experimental evidences, including dielectric permittivity, heat capacity, and thermal conductivity measured down to 66 mK, to reveal the existence of a nontrivial ground state in BaFe12O19, characterized by itinerant low-energy excitations with a small gap, to which we interpret as an exotic liquid-like quantum phase. The quantum electric-dipole liquids in frustrated dielectrics open up a fresh playground for fundamental physics and may find applications in quantum information and computation as well.Comment: 13 pages, 6 figure

Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs

How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state-of-the-art performance in many node classification tasks. Yet, combining GNNs with LMs has not been widely explored for practical deployments due to its scalability issues. In this work, we tackle this challenge by developing a Graph-Aware Distillation framework (GRAD) to encode graph structures into an LM for graph-free, fast inference. Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM. This encourages the graph-free student to exploit graph information encoded by the GNN teacher while at the same time, enables the GNN teacher to better leverage textual information from unlabeled nodes. As a result, the teacher and the student models learn from each other to improve their overall performance. Experiments in eight node classification benchmarks in both transductive and inductive settings showcase GRAD's superiority over existing distillation approaches for textual graphs

Jets in a Gamma-Ray Burst During its Prompt Emission: Evolution of Lorentz Factor

Knowledge about the Lorentz factor and its evolution of relativistic jets in gamma-ray bursts (GRBs) is crucial to understand their physics. An exact value of bulk Lorentz factor can be estimated based on a high-energy spectral cutoff, which may appear in GRBs' prompt emission owing to the absorption of photon-photon pair production. In this work, we focus on the investigation of the bulk Lorentz factor evolution of jets in an individual burst. Based on \textsl{Fermi} observations, we search for the bursts with multiple $\gamma$-ray pulses characterized by a high-energy spectral cutoff, and nine GRBs are obtained. Together with the estimation of the pulse duration and radiation spectrum, the Lorentz factor of jets corresponding to different pulses in an individual GRB are estimated. It is shown that the Lorentz factor of jets in an individual GRB fluctuates within a certain range and without a general trend in these nine GRBs. In addition, the Lorentz factors of the jets in GRBs~130821A, 160509A and 160625B seem to increase with time. We also study the relations among $L_{\rm iso }$, $E_{\rm p,z}$, and $\Gamma$ for the pulses in our sample, which is found to be consistent with that found in previous works.Comment: Accepted for publication in Ap