Magnetic order in pyrochlore iridate Nd2_2Ir2_2O7_7 probed by muon spin relaxation

Muon-spin relaxation results on the pyrochlore iridate Nd$_2$Ir$_2$O$_7$ 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$^{4+}$ 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$^{3+}$ moments, which is consistent with a previous neutron experiment. Our results suggest that the MIT and magnetic ordering of Ir$^{4+}$ 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 $\chi_{ac}(T)$ 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