87 research outputs found
Magnetic order in pyrochlore iridate NdIrO probed by muon spin relaxation
Muon-spin relaxation results on the pyrochlore iridate NdIrO are
reported. Spontaneous coherent muon-spin precession below the metal-insulator
transition (MIT) temperature of about 33 K is observed, indicating the
appearance of a long-ranged magnetic ordering of Ir moments. With
further decrease in temperature, the internal field at the muon site increases
again below about 9 K. The second increase of internal field suggests the
ordering of Nd moments, which is consistent with a previous neutron
experiment. Our results suggest that the MIT and magnetic ordering of Ir
moments have a close relationship and that the large spin-orbit coupling of Ir
5\textit{d} electrons plays a key role for both MIT and the mechanism of the
magnetic ordering in pyrochlore iridates in the insulting ground state.Comment: 5 pages, 3 figures. Accepted by Physical Review B (rapid
communications
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation
Unsupervised domain adaptation (UDA) involves adapting a model trained on a
label-rich source domain to an unlabeled target domain. However, in real-world
scenarios, the absence of target-domain labels makes it challenging to evaluate
the performance of deep models after UDA. Additionally, prevailing UDA methods
typically rely on adversarial training and self-training, which could lead to
model degeneration and negative transfer, further exacerbating the evaluation
problem. In this paper, we propose a novel metric called the \textit{Transfer
Score} to address these issues. The transfer score enables the unsupervised
evaluation of domain adaptation models by assessing the spatial uniformity of
the classifier via model parameters, as well as the transferability and
discriminability of the feature space. Based on unsupervised evaluation using
our metric, we achieve three goals: (1) selecting the most suitable UDA method
from a range of available options, (2) optimizing hyperparameters of UDA models
to prevent model degeneration, and (3) identifying the epoch at which the
adapted model performs optimally. Our work bridges the gap between UDA research
and practical UDA evaluation, enabling a realistic assessment of UDA model
performance. We validate the effectiveness of our metric through extensive
empirical studies conducted on various public datasets. The results demonstrate
the utility of the transfer score in evaluating UDA models and its potential to
enhance the overall efficacy of UDA techniques
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Due to domain shift, a large performance drop is usually observed when a
trained crowd counting model is deployed in the wild. While existing
domain-adaptive crowd counting methods achieve promising results, they
typically regard each crowd image as a whole and reduce domain discrepancies in
a holistic manner, thus limiting further improvement of domain adaptation
performance. To this end, we propose to untangle \emph{domain-invariant} crowd
and \emph{domain-specific} background from crowd images and design a
fine-grained domain adaption method for crowd counting. Specifically, to
disentangle crowd from background, we propose to learn crowd segmentation from
point-level crowd counting annotations in a weakly-supervised manner. Based on
the derived segmentation, we design a crowd-aware domain adaptation mechanism
consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer
(CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide
crowd features transfer across domains beyond background distractions. The CDA
module dedicates to regularising target-domain crowd density generation by its
own crowd density distribution. Our method outperforms previous approaches
consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table
Emergent order in the spin-frustrated system DyxTb2-xTi2O7 studied by ac susceptibility measurements
We report the a.c. susceptibility study of Dy_xTb_{2-x}Ti_2O_7 with x in [0,
2]. In addition to the single-ion effect at Ts (single-ion effect peak
temperature) corresponding to the Dy3+ spins as that in spin ice Dy_2Ti_2O_7
and a possible spin freezing peak at Tf (Tf < 3 K), a new peak associated with
Tb^{3+} is observed in at nonzero magnetic field with a
characteristic temperature T^* (Tf < T^* < Ts). T^* increases linearly with x
in a wide composition range (0 < x < 1.5 at 5 kOe). Both application of a
magnetic field and increasing doping with Dy3+ enhance T^*. The T^* peak is
found to be thermally driven with an unusually large energy barrier as
indicated from its frequency dependence. These effects are closely related to
the crystal field levels, and the underlying mechanism remains to be
understood.Comment: 7 pages, 5 figure
The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning
In recent years, short Text Matching tasks have been widely applied in the
fields ofadvertising search and recommendation. The difficulty lies in the lack
of semantic information and word ambiguity caused by the short length of the
text. Previous works have introduced complement sentences or knowledge bases to
provide additional feature information. However, these methods have not fully
interacted between the original sentence and the complement sentence, and have
not considered the noise issue that may arise from the introduction of external
knowledge bases. Therefore, this paper proposes a short Text Matching model
that combines contrastive learning and external knowledge. The model uses a
generative model to generate corresponding complement sentences and uses the
contrastive learning method to guide the model to obtain more semantically
meaningful encoding of the original sentence. In addition, to avoid noise, we
use keywords as the main semantics of the original sentence to retrieve
corresponding knowledge words in the knowledge base, and construct a knowledge
graph. The graph encoding model is used to integrate the knowledge base
information into the model. Our designed model achieves state-of-the-art
performance on two publicly available Chinese Text Matching datasets,
demonstrating the effectiveness of our model.Comment: 11 pages,2 figure
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
Recent advances in Large Language Models (LLMs) have achieved remarkable
breakthroughs in understanding and responding to user intents. However, their
performance lag behind general use cases in some expertise domains, such as
Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs
rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue
data. These models lack the ability for doctor-like proactive inquiry and
multi-turn comprehension and cannot always align responses with safety and
professionalism experts. In this work, we introduce Zhongjing, the first
Chinese medical LLaMA-based LLM that implements an entire training pipeline
from pre-training to reinforcement learning with human feedback (RLHF).
Additionally, we introduce a Chinese multi-turn medical dialogue dataset of
70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly
enhances the model's capability for complex dialogue and proactive inquiry
initiation. We define a refined annotation rule and evaluation criteria given
the biomedical domain's unique characteristics. Results show that our model
outperforms baselines in various capacities and matches the performance of
ChatGPT in a few abilities, despite having 50x training data with previous best
model and 100x parameters with ChatGPT. RLHF further improves the model's
instruction-following ability and safety.We also release our code, datasets and
model for further research
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