Electron heat flux and propagating fronts in plasma thermal quench via ambipolar transport

The thermal collapse of a nearly collisionless plasma interacting with a cooling spot, in which the electron parallel heat flux plays an essential role, is investigated both theoretically and numerically. We show that such thermal collapse, which is known as thermal quench in tokamaks, comes about in the form of propagating fronts, originating from the cooling spot, along the magnetic field lines. The slow fronts, propagating with local ion sound speed, limit the aggressive cooling of plasma, which is accompanied by a plasma cooling flow toward the cooling spot. The extraordinary physics underlying such a cooling flow is that the fundamental constraint of ambipolar transport along the field line limits the spatial gradient of electron thermal conduction flux to the much weaker convective scaling, as opposed to the free-streaming scaling, so that a large electron temperature and hence pressure gradient can be sustained. The last ion front for a radiative cooling spot is a shock front where cold but flowing ions meet the hot ions

Resolving the mystery of electron perpendicular temperature spike in the plasma sheath

A large family of plasmas has collisional mean-free-path much longer than the non-neutral sheath width, which scales with the plasma Debye length. The plasmas, particularly the electrons, assume strong temperature anisotropy in the sheath. The temperature in the sheath flow direction ($T_{e\parallel}$) is lower and drops towards the wall as a result of the decompressional cooling by the accelerating sheath flow. The electron temperature in the transverse direction of the flow field ($T_{e\perp}$) not only is higher but also spikes up in the sheath. This abnormal behavior of $T_{e\perp}$ spike is found to be the result of a negative gradient of the parallel heat flux of transverse degrees of freedom ($q_{es}$) in the sheath. The non-zero heat flux $q_{es}$ is induced by pitch-angle scattering of electrons via either their interaction with self-excited electromagnetic waves in a nearly collisionless plasma or Coulomb collision in a collisional plasma, or both in the intermediate regime of plasma collisionality

Staged cooling of a fusion-grade plasma in a tokamak thermal quench

In tokamak disruptions where the magnetic connection length becomes comparable to or even shorter than the plasma mean-free-path, parallel transport can dominate the energy loss and the thermal quench of the core plasma goes through four phases (stages) that have distinct temperature ranges and durations. The main temperature drop occurs while the core plasma remains nearly collisionless, with the parallel electron temperature $T_{e\parallel}$ dropping in time $t$ as $T_{e\parallel}\propto t^{-2}$ and a cooling time that scales with the ion sound wave transit time over the length of the open magnetic field line. These surprising physics scalings are the result of effective suppression of parallel electron thermal conduction in an otherwise bounded collisionless plasma, which is fundamentally different from what are known to date on electron thermal conduction along the magnetic field in a nearly collisionless plasma

VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph

Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimodal relationships like semantic similarity, spatial relations, etc. We first explored this issue by organizing a large-scale scene graph dataset, namely Visual Genome, in the RDF graph database. Based on the proposed RDF-stored multimodal scene graph, we extended SPARQL queries to answer questions containing relational reasoning about color, spatial, etc. Further demo (i.e., VGStore) shows the effectiveness of customized queries and displaying multimodal data.Comment: ISWC 2022 Posters, Demos, and Industry Track

LLMaAA: Making Large Language Models as Active Annotators

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acquiring such data is a costly endeavor. Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the training data are solely synthesized from LLMs. However, such an approach usually suffers from low-quality issues, and requires orders of magnitude more labeled data to achieve satisfactory performance. To fully exploit the potential of LLMs and make use of massive unlabeled data, we propose LLMaAA, which takes LLMs as annotators and puts them into an active learning loop to determine what to annotate efficiently. To learn robustly with pseudo labels, we optimize both the annotation and training processes: (1) we draw k-NN examples from a small demonstration pool as in-context examples, and (2) we adopt the example reweighting technique to assign training samples with learnable weights. Compared with previous approaches, LLMaAA features both efficiency and reliability. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples, which is much more cost-effective than other baselines.Comment: Findings of EMNLP 2023 camera read