105 research outputs found
Evaluate Geometry of Radiance Field with Low-frequency Color Prior
Radiance field is an effective representation of 3D scenes, which has been
widely adopted in novel-view synthesis and 3D reconstruction. It is still an
open and challenging problem to evaluate the geometry, i.e., the density field,
as the ground-truth is almost impossible to be obtained. One alternative
indirect solution is to transform the density field into a point-cloud and
compute its Chamfer Distance with the scanned ground-truth. However, many
widely-used datasets have no point-cloud ground-truth since the scanning
process along with the equipment is expensive and complicated. To this end, we
propose a novel metric, named Inverse Mean Residual Color (IMRC), which can
evaluate the geometry only with the observation images. Our key insight is that
the better the geometry is, the lower-frequency the computed color field is.
From this insight, given reconstructed density field and the observation
images, we design a closed-form method to approximate the color field with
low-frequency spherical harmonics and compute the inverse mean residual color.
Then the higher the IMRC, the better the geometry. Qualitative and quantitative
experimental results verify the effectiveness of our proposed IMRC metric. We
also benchmark several state-of-the-art methods using IMRC to promote future
related research.Comment: 20 page
Quartet Logic: A Four-Step Reasoning (QLFR) framework for advancing Short Text Classification
Short Text Classification (STC) is crucial for processing and comprehending
the brief but substantial content prevalent on contemporary digital platforms.
The STC encounters difficulties in grasping semantic and syntactic intricacies,
an issue that is apparent in traditional pre-trained language models. Although
Graph Convolutional Networks enhance performance by integrating external
knowledge bases, these methods are limited by the quality and extent of the
knowledge applied. Recently, the emergence of Large Language Models (LLMs) and
Chain-of-Thought (CoT) has significantly improved the performance of complex
reasoning tasks. However, some studies have highlighted the limitations of
their application in fundamental NLP tasks. Consequently, this study sought to
employ CoT to investigate the capabilities of LLMs in STC tasks. This study
introduces Quartet Logic: A Four-Step Reasoning (QLFR) framework. This
framework primarily incorporates Syntactic and Semantic Enrichment CoT,
effectively decomposing the STC task into four distinct steps: (i) essential
concept identification, (ii) common-sense knowledge retrieval, (iii) text
rewriting, and (iv) classification. This elicits the inherent knowledge and
abilities of LLMs to address the challenges in STC. Surprisingly, we found that
QLFR can also improve the performance of smaller models. Therefore, we
developed a CoT-Driven Multi-task learning (QLFR-CML) method to facilitate the
knowledge transfer from LLMs to smaller models. Extensive experimentation
across six short-text benchmarks validated the efficacy of the proposed
methods. Notably, QLFR achieved state-of-the-art performance on all datasets,
with significant improvements, particularly on the Ohsumed and TagMyNews
datasets
PRECISION.array: An R Package for Benchmarking microRNA Array Data Normalization in the Context of Sample Classification
We present a new R package PRECISION.array for assessing the performance of data normalization methods in connection with methods for sample classification. It includes two microRNA microarray datasets for the same set of tumor samples: a re-sampling-based algorithm for simulating additional paired datasets under various designs of sample-to-array assignment and levels of signal-to-noise ratios and a collection of numerical and graphical tools for method performance assessment. The package allows users to specify their own methods for normalization and classification, in addition to implementing three methods for training data normalization, seven methods for test data normalization, seven methods for classifier training, and two methods for classifier validation. It enables an objective and systemic evaluation of the operating characteristics of normalization and classification methods in microRNA microarrays. To our knowledge, this is the first such tool available. The R package can be downloaded freely at https://github.com/LXQin/PRECISION.array
- …