1,405 research outputs found
Electronic structure of RSb (R = Y, Ce, Gd, Dy, Ho, Tm, Lu) studied by angle-resolved photoemission spectroscopy
We use high resolution angle-resolved photoemission spectroscopy (ARPES) and
electronic structure calculations to study the electronic properties of
rare-earth monoantimonides RSb (R = Y, Ce, Gd, Dy, Ho, Tm, Lu). The
experimentally measured Fermi surface (FS) of RSb consists of at least two
concentric hole pockets at the point and two intersecting electron
pockets at the point. These data agree relatively well with the electronic
structure calculations. Detailed photon energy dependence measurements using
both synchrotron and laser ARPES systems indicate that there is at least one
Fermi surface sheet with strong three-dimensionality centered at the
point. Due to the "lanthanide contraction", the unit cell of different
rare-earth monoantimonides shrinks when changing rare-earth ion from CeSb to
LuSb. This results in the differences in the chemical potentials in these
compounds, which is demonstrated by both ARPES measurements and electronic
structure calculations. Interestingly, in CeSb, the intersecting electron
pockets at the point seem to be touching the valence bands, forming a
four-fold degenerate Dirac-like feature. On the other hand, the remaining
rare-earth monoantimonides show significant gaps between the upper and lower
bands at the point. Furthermore, similar to the previously reported results
of LaBi, a Dirac-like structure was observed at the point in YSb,
CeSb, and GdSb, compounds showing relatively high magnetoresistance. This
Dirac-like structure may contribute to the unusually large magnetoresistance in
these compounds.Comment: 8 figure
Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection
Arbitrary-oriented object detection is a relatively emerging but challenging
task. Although remarkable progress has been made, there still remain many
unsolved issues due to the large diversity of patterns in orientation, scale,
aspect ratio, and visual appearance of objects in aerial images. Most of the
existing methods adopt a coarse-grained fixed label assignment strategy and
suffer from the inconsistency between the classification score and localization
accuracy. First, to align the metric inconsistency between sample selection and
regression loss calculation caused by fixed IoU strategy, we introduce affine
transformation to evaluate the quality of samples and propose a distance-based
label assignment strategy. The proposed metric-aligned selection (MAS) strategy
can dynamically select samples according to the shape and rotation
characteristic of objects. Second, to further address the inconsistency between
classification and localization, we propose a critical feature sampling (CFS)
module, which performs localization refinement on the sampling location for
classification task to extract critical features accurately. Third, we present
a scale-controlled smooth loss (SC-Loss) to adaptively select high
quality samples by changing the form of regression loss function based on the
statistics of proposals during training. Extensive experiments are conducted on
four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016,
and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed
detector
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
Multimodal sentiment analysis (MSA) and emotion recognition in conversation
(ERC) are key research topics for computers to understand human behaviors. From
a psychological perspective, emotions are the expression of affect or feelings
during a short period, while sentiments are formed and held for a longer
period. However, most existing works study sentiment and emotion separately and
do not fully exploit the complementary knowledge behind the two. In this paper,
we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that
unifies MSA and ERC tasks from features, labels, and models. We perform
modality fusion at the syntactic and semantic levels and introduce contrastive
learning between modalities and samples to better capture the difference and
consistency between sentiments and emotions. Experiments on four public
benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the
effectiveness of the proposed method and achieve consistent improvements
compared with state-of-the-art methods.Comment: Accepted to EMNLP 2022 main conferenc
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Recent research has shown that multi-task pre-training greatly improves the
model's robustness and transfer ability, which is crucial for building a
high-quality dialog system. However, most previous works on multi-task
pre-training rely heavily on human-defined input format or prompt, which is not
optimal in quality and quantity. In this work, we propose to use Task-based
Automatic Prompt generation (TAP) to automatically generate high-quality
prompts. Using the high-quality prompts generated, we scale the corpus of the
pre-trained conversation model to 122 datasets from 15 dialog-related tasks,
resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful
foundation model for various conversational tasks and different dialog systems.
Extensive experiments have shown that UniPCM is robust to input prompts and
capable of various dialog-related tasks. Moreover, UniPCM has strong transfer
ability and excels at low resource scenarios, achieving SOTA results on 9
different datasets ranging from task-oriented dialog to open-domain
conversation. Furthermore, we are amazed to find that TAP can generate prompts
on par with those collected with crowdsourcing. The code is released with the
paper
Evaluation of the estimate bias magnitude of the Rao’s quadratic diversity index
Rao’s quadratic diversity index is one of the most widely applied diversity indices in functional and phylogenetic ecology. The standard way of computing Rao’s quadratic diversity index for an ecological assemblage with a group of species with varying abundances is to sum the functional or phylogenetic distances between a pair of species in the assemblage, weighted by their relative abundances. Here, using both theoretically derived and observed empirical datasets, we show that this standard calculation routine in practical applications will statistically underestimate the true value, and the bias magnitude is derived accordingly. The underestimation will become worse when the studied ecological community contains more species or the pairwise species distance is large. For species abundance data measured using the number of individuals, we suggest calculating the unbiased Rao’s quadratic diversity index
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Task-oriented dialogue (TOD) systems facilitate users in executing various
activities via multi-turn dialogues, but Large Language Models (LLMs) often
struggle to comprehend these intricate contexts. In this study, we propose a
novel "Self-Explanation" prompting strategy to enhance the comprehension
abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires
the model to analyze each dialogue utterance before task execution, thereby
improving performance across various dialogue-centric tasks. Experimental
results from six benchmark datasets confirm that our method consistently
outperforms other zero-shot prompts and matches or exceeds the efficacy of
few-shot prompts, demonstrating its potential as a powerful tool in enhancing
LLMs' comprehension in complex dialogue tasks
UniSA: Unified Generative Framework for Sentiment Analysis
Sentiment analysis is a crucial task that aims to understand people's
emotional states and predict emotional categories based on multimodal
information. It consists of several subtasks, such as emotion recognition in
conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal
sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis
presents numerous challenges, including modality alignment, unified
input/output forms, and dataset bias. To address these challenges, we propose a
Task-Specific Prompt method to jointly model subtasks and introduce a
multimodal generative framework called UniSA. Additionally, we organize the
benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation
benchmark, SAEval. We design novel pre-training tasks and training methods to
enable the model to learn generic sentiment knowledge among subtasks to improve
the model's multimodal sentiment perception ability. Our experimental results
show that UniSA performs comparably to the state-of-the-art on all subtasks and
generalizes well to various subtasks in sentiment analysis.Comment: Accepted to ACM MM 202
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy
user expressions, and the shortage of annotated data. To better solve the above
problems, we propose CGoDial, new challenging and comprehensive Chinese
benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763
dialog sessions and 574,949 dialog turns totally, covering three datasets with
different knowledge sources: 1) a slot-based dialog (SBD) dataset with
table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed
knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed
knowledge. To bridge the gap between academic benchmarks and spoken dialog
scenarios, we either collect data from real conversations or add spoken
features to existing datasets via crowd-sourcing. The proposed experimental
settings include the combinations of training with either the entire training
set or a few-shot training set, and testing with either the standard test set
or a hard test subset, which can assess model capabilities in terms of general
prediction, fast adaptability and reliable robustness.Comment: EMNLP 202
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