DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues

Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation labels for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that this task is challenging for existing models and the dataset will be useful for future research.Comment: This paper has been accepted by AAAI202

Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.Comment: findings of ACL'2

Absence of a transport signature of spin-orbit coupling in graphene with indium adatoms

Enhancement of the spin-orbit coupling in graphene may lead to various topological phenomena and also find applications in spintronics. Adatom absorption has been proposed as an effective way to achieve the goal. In particular, great hope has been held for indium in strengthening the spin-orbit coupling and realizing the quantum spin Hall effect. To search for evidence of the spin-orbit coupling in graphene absorbed with indium adatoms, we carry out extensive transport measurements, i.e., weak localization magnetoresistance, quantum Hall effect and non-local spin Hall effect. No signature of the spin-orbit coupling is found. Possible explanations are discussed.Comment: 5 pages, 4 figures, with supplementary material

In-sample Curriculum Learning by Sequence Completion for Natural Language Generation

Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by the ``easy-to-hard'' intuition, we propose to do in-sample curriculum learning for natural language generation tasks. Our learning strategy starts training the model to generate the last few words, i.e., do sequence completion, and gradually extends to generate the whole output sequence. Comprehensive experiments show that it generalizes well to different tasks and achieves significant improvements over strong baselines

Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration

The 3D Gaussian Splatting (3DGS) gained its popularity recently by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free, and its rendering at varying resolutions could produce severe blurring or jaggies. This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels. Consequently, this discrete sampling scheme inevitably results in aliasing, owing to the restricted sampling bandwidth. In this paper, we derive an analytical solution to address this issue. More specifically, we use a conditioned logistic function as the analytic approximation of the cumulative distribution function (CDF) in a one-dimensional Gaussian signal and calculate the Gaussian integral by subtracting the CDFs. We then introduce this approximation in the two-dimensional pixel shading, and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area to better capture the intensity response of each pixel. Moreover, we use the approximated response of the pixel window integral area to participate in the transmittance calculation of volume rendering, making Analytic-Splatting sensitive to the changes in pixel footprint at different resolutions. Experiments on various datasets validate that our approach has better anti-aliasing capability that gives more details and better fidelity.Comment: 29 page

Effects of matrine on collagen proliferation and TNF-α, TGF-β1 and CTGF in atrial tissues of dogs with persistent atrial fibrillation

目的 探讨苦参碱对犬心房颤动（房颤）心房肌组织中胶原合成以及肿瘤坏死因子（tumor necrosis factor alpha,TNF-α）、转化生长因子（transforming growth factor-β1，TGF-β1）和结缔组织生长因子（connective Tissue Growth Factor，CTGF）表达变化的影响。方法 健康比格犬10只采用快速右心室起搏造房颤模型，随机分成房颤组和房颤+苦参碱组各5只。采用天狼星红染色，计算胶原容积分数（collagen volume fraction，CVF）以测定纤维化程度；采用免疫组织化学法检测右心房TNF-α、TGF-β1和CTGF的蛋白表达情况；用逆转录-聚合酶链反应（RT-PCR）技术检测TNF-α、TGF-β1和CTGF的mRNA水平表达情况。结果 与房颤组相比，房颤+苦参碱组纤维化程度降低，CVF明显下降（P＜0.05），TNF-α、TGF-β1和CTGF蛋白表达水平下降，且TNF-α和TGF-β1的mRNA表达水平显著下降（P＜0.05，P＜0.01）。结论 苦参碱可能通过抑制TNF-α、TGF-β1和CTGF的表达，抑制房颤心房肌胶原合成，改善心房组织纤维化程度。Objective:To study the effects of matrine (mat) on collagen synthesis and expression of tumor necrosis factoralpha (TNF-α), and transforming growth factor-β1 (TGF-β1) and connective tissue growth factor (CTGF) in atrial tissues of dogs with persistent atrial fibrillation (AF). Methods : Ten healthy beagle dogs were randomly divided into two groups: AF group (n=5) and AF/Mat group (n=5), using right ventricular pacing to establish AF model. The collagen volume fraction (CVF) in atrial tissue were detected by sirius red staining to determine the level of fabrication. The level of TNF-α, TGF-β1 and CTGF were detected by immunohisto-chemistry. The mRNA expression level of TNF-α, TGF-β1 and CTGF were detected by reverse transcription-polymerase chain reaction (RT-PCR). Results:  Compared with the AF group, the fabriation level of AF/Mat was decreased obviously (P<0.05), the expression levels of TNF-α, TGF-β1 and CTGF were decreased, and the mRNA expression level were decreased significantly in atrial tissues (P<0.05 and P<0.01). Conclusion: Matrine may inhibits fabrosis in atrial tissues through inhibition collagen proliferation and expression of TNF-α, TGF-β1 and CTGF