80 research outputs found
Fourier-transformed gauge theory models of three-dimensional topological orders with gapped boundaries
In this paper, we apply the method of Fourier transform and basis rewriting
developed in arXiv:1910.13441 for the two-dimensional quantum double model of
topological orders to the three-dimensional gauge theory model (with a gauge
group ) of three-dimensional topological orders. We find that the gapped
boundary condition of the gauge theory model is characterized by a Frobenius
algebra in the representation category of , which also
describes the charge splitting and condensation on the boundary. We also show
that our Fourier transform maps the three-dimensional gauge theory model with
input data to the Walker-Wang model with input data on a
trivalent lattice with dangling edges, after truncating the Hilbert space by
projecting all dangling edges to the trivial representation of . This
Fourier transform also provides a systematic construction of the gapped
boundary theory of the Walker-Wang model. This establishes a correspondence
between two types of topological field theories: the extended Dijkgraaf-Witten
and extended Crane-Yetter theories.Comment: 39 pages, 9 figure
Task-Agnostic Structured Pruning of Speech Representation Models
Self-supervised pre-trained models such as Wav2vec2, Hubert, and WavLM have
been shown to significantly improve many speech tasks. However, their large
memory and strong computational requirements hinder their industrial
applicability. Structured pruning is a hardware-friendly model compression
technique but usually results in a larger loss of accuracy. In this paper, we
propose a fine-grained attention head pruning method to compensate for the
performance degradation. In addition, we also introduce the straight through
estimator into the L0 regularization to further accelerate the pruned model.
Experiments on the SUPERB benchmark show that our model can achieve comparable
performance to the dense model in multiple tasks and outperforms the Wav2vec
2.0 base model on average, with 72% fewer parameters and 2 times faster
inference speed.Comment: Accepted by INTERSPEECH 202
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Large language models (LLMs) have garnered significant attention due to their
impressive natural language processing (NLP) capabilities. Recently, many
studies have focused on the tool utilization ability of LLMs. They primarily
investigated how LLMs effectively collaborate with given specific tools.
However, in scenarios where LLMs serve as intelligent agents, as seen in
applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate
decision-making processes that involve deciding whether to employ a tool and
selecting the most suitable tool(s) from a collection of available tools to
fulfill user requests. Therefore, in this paper, we introduce MetaTool, a
benchmark designed to evaluate whether LLMs have tool usage awareness and can
correctly choose tools. Specifically, we create a dataset called ToolE within
the benchmark. This dataset contains various types of user queries in the form
of prompts that trigger LLMs to use tools, including both single-tool and
multi-tool scenarios. Subsequently, we set the tasks for both tool usage
awareness and tool selection. We define four subtasks from different
perspectives in tool selection, including tool selection with similar choices,
tool selection in specific scenarios, tool selection with possible reliability
issues, and multi-tool selection. We conduct experiments involving nine popular
LLMs and find that the majority of them still struggle to effectively select
tools, highlighting the existing gaps between LLMs and genuine intelligent
agents. However, through the error analysis, we found there is still
significant room for improvement. Finally, we conclude with insights for tool
developers that follow ChatGPT to provide detailed descriptions that can
enhance the tool selection performance of LLMs
Validity of patients' online reviews at direct-to-consumer teleconsultation platforms:a protocol for a cross-sectional study using unannounced standardised patients
Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH -Mutant Molecular Profiles
Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance
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