3,708 research outputs found
Topolgical Charged Black Holes in Generalized Horava-Lifshitz Gravity
As a candidate of quantum gravity in ultrahigh energy, the
-dimensional Ho\v{r}ava-Lifshitz (HL) gravity with critical exponent
, indicates anisotropy between time and space at short distance. In the
paper, we investigate the most general Ho\v{r}ava-Lifshitz gravity in
arbitrary spatial dimension , with a generic dynamical Ricci flow parameter
and a detailed balance violation parameter . In arbitrary
dimensional generalized HL gravity with at long distance, we
study the topological neutral black hole solutions with general in
HL, as well as the topological charged black holes with
in HL. The HL gravity in the Lagrangian formulation
is adopted, while in the Hamiltonian formulation, it reduces to DiracDe
Witt's canonical gravity with . In particular, the topological
charged black holes in HL, HL, HL and
HL with are solved. Their asymptotical behaviors near the
infinite boundary and near the horizon are explored respectively. We also study
the behavior of the topological black holes in the -dimensional HL
gravity with gauge field in the zero temperature limit and finite
temperature limit, respectively. Thermodynamics of the topological charged
black holes with , including temperature, entropy, heat capacity,
and free energy are evaluated.Comment: 51 pages, published version. The theoretical framework of z=d HL
gravity is set up, and higher curvature terms in spatial dimension become
relevant at UV fixed point. Lovelock term, conformal term, new massive term,
and Chern-Simons term with different critical exponent z are studie
An Exploration Study of Mixed-initiative Query Reformulation in Conversational Passage Retrieval
In this paper, we report our methods and experiments for the TREC
Conversational Assistance Track (CAsT) 2022. In this work, we aim to reproduce
multi-stage retrieval pipelines and explore one of the potential benefits of
involving mixed-initiative interaction in conversational passage retrieval
scenarios: reformulating raw queries. Before the first ranking stage of a
multi-stage retrieval pipeline, we propose a mixed-initiative query
reformulation module, which achieves query reformulation based on the
mixed-initiative interaction between the users and the system, as the
replacement for the neural reformulation method. Specifically, we design an
algorithm to generate appropriate questions related to the ambiguities in raw
queries, and another algorithm to reformulate raw queries by parsing users'
feedback and incorporating it into the raw query. For the first ranking stage
of our multi-stage pipelines, we adopt a sparse ranking function: BM25, and a
dense retrieval method: TCT-ColBERT. For the second-ranking step, we adopt a
pointwise reranker: MonoT5, and a pairwise reranker: DuoT5. Experiments on both
TREC CAsT 2021 and TREC CAsT 2022 datasets show the effectiveness of our
mixed-initiative-based query reformulation method on improving retrieval
performance compared with two popular reformulators: a neural reformulator:
CANARD-T5 and a rule-based reformulator: historical query reformulator(HQE).Comment: The Thirty-First Text REtrieval Conference (TREC 2022) Proceeding
Zero-shot Query Reformulation for Conversational Search
As the popularity of voice assistants continues to surge, conversational
search has gained increased attention in Information Retrieval. However, data
sparsity issues in conversational search significantly hinder the progress of
supervised conversational search methods. Consequently, researchers are
focusing more on zero-shot conversational search approaches. Nevertheless,
existing zero-shot methods face three primary limitations: they are not
universally applicable to all retrievers, their effectiveness lacks sufficient
explainability, and they struggle to resolve common conversational ambiguities
caused by omission. To address these limitations, we introduce a novel
Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based
on previous dialogue contexts without requiring supervision from conversational
search data. Specifically, our framework utilizes language models designed for
machine reading comprehension tasks to explicitly resolve two common
ambiguities: coreference and omission, in raw queries. In comparison to
existing zero-shot methods, our approach is universally applicable to any
retriever without additional adaptation or indexing. It also provides greater
explainability and effectively enhances query intent understanding because
ambiguities are explicitly and proactively resolved. Through extensive
experiments on four TREC conversational datasets, we demonstrate the
effectiveness of our method, which consistently outperforms state-of-the-art
baselines.Comment: Accepted by the 9th ACM SIGIR International Conference on the Theory
of Information Retrieva
Auslander-Reiten translations in monomorphism categories
We generalize Ringel and Schmidmeier's theory on the Auslander-Reiten
translation of the submodule category to the monomorphism
category . As in the case of , has
Auslander-Reiten sequences, and the Auslander-Reiten translation
of can be explicitly formulated via
of -mod. Furthermore, if is a selfinjective algebra, we study the
periodicity of on the objects of , and of
the Serre functor on the objects of the stable monomorphism
category . In particular, for X\in\mathcal{S}_n(\A(m, t)); and for X\in\underline{\mathcal{S}_n(\A(m, t))}, where
\A(m, t), \ m\ge1, \ t\ge2, are the selfinjective Nakayama algebras.Comment: 33 pages, 1 figure
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