MotionChain: Conversational Motion Controllers via Multimodal Prompts

Recent advancements in language models have demonstrated their adeptness in conducting multi-turn dialogues and retaining conversational context. However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movements, generative human motion models can achieve an intuitive and step-by-step process of human task execution for humanoid robotics, game agents, or other embodied systems. In this work, we present MotionChain, a conversational human motion controller to generate continuous and long-term human motion through multimodal prompts. Specifically, MotionChain consists of multi-modal tokenizers that transform various data types such as text, image, and motion, into discrete tokens, coupled with a Vision-Motion-aware Language model. By leveraging large-scale language, vision-language, and vision-motion data to assist motion-related generation tasks, MotionChain thus comprehends each instruction in multi-turn conversation and generates human motions followed by these prompts. Extensive experiments validate the efficacy of MotionChain, demonstrating state-of-the-art performance in conversational motion generation, as well as more intuitive manners of controlling and interacting with virtual humans.Comment: 14 pages, 4 figure

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patterns. Since these modification patterns could also be combined with other SQL statements, the models are supposed to have the compositional generalization to these novel combinations. This work is the first exploration of compositional generalization in context-dependent Text-to-SQL scenarios. To facilitate related studies, we constructed two challenging benchmarks named \textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification patterns and existing SQL statements. The following experiments show that all current models struggle on our proposed benchmarks. Furthermore, we found that better aligning the previous SQL statements with the input utterance could give models better compositional generalization ability. Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models. Further experiments validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. Metric-based meta-learning is an effective framework widely adopted for FSDLRE, which constructs class prototypes for classification. However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype. 2) They use a set of generic NOTA (none-of-the-above) prototypes across all tasks, neglecting that the NOTA semantics differs in tasks with different target relation types. In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. By judiciously leveraging the relation descriptions and realistic NOTA instances as guidance, our method effectively refines the relation prototypes and generates task-specific NOTA prototypes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by average 2.61% $F_1$ across various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202

Stellar cycle and evolution of polar spots in an M+WD binary

Stellar activity cycles reveal continuous relaxation and induction of magnetic fields. The activity cycle is typically traced through the observation of cyclic variations in total brightness or Ca H&K emission flux of stars, as well as cyclic variations of orbital periods of binary systems. In this work, we report the identification of a semi-detached binary system (TIC 16320250) consisting of a white dwarf (0.67 $M_{\odot}$) and an active M dwarf (0.56 $M_{\odot}$). The long-term multi-band optical light curves spanning twenty years revealed three repeated patterns, suggestive of a possible activity cycle of about ten years of the M dwarf. Light curve fitting indicates the repeated variation is caused by the evolution, particularly the motion, of polar spots. The significant Ca H&K, H$\alpha$, ultra-violet, and X-ray emissions imply that the M dwarf is one of the most magnetically active stars. We propose that in the era of large time-domain photometric sky surveys (e.g., ASAS-SN, ZTF, LSST, Sitian), long-term light curve modeling can be a valuable tool for tracing and revealing stellar activity cycle, especially for stars in binary systems.Comment: 17 pages, 14 figures, accepted for publication in AP

Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison

Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models

Mutations in an AP2 Transcription Factor-Like Gene Affect Internode Length and Leaf Shape in Maize

Background Plant height is an important agronomic trait that affects yield and tolerance to certain abiotic stresses. Understanding the genetic control of plant height is important for elucidating the regulation of maize development and has practical implications for trait improvement in plant breeding. Methodology/Principal Findings In this study, two independent, semi-dwarf maize EMS mutants, referred to as dwarf & irregular leaf (dil1), were isolated and confirmed to be allelic. In comparison to wild type plants, the mutant plants have shorter internodes, shorter, wider and wrinkled leaves, as well as smaller leaf angles. Cytological analysis indicated that the leaf epidermal cells and internode parenchyma cells are irregular in shape and are arranged in a more random fashion, and the mutants have disrupted leaf epidermal patterning. In addition, parenchyma cells in the dil1 mutants are significantly smaller than those in wild-type plants. The dil1 mutation was mapped on the long arm of chromosome 6 and a candidate gene, annotated as an AP2 transcription factor-like, was identified through positional cloning. Point mutations near exon-intron junctions were identified in both dil1 alleles, resulting in mis-spliced variants. Conclusion An AP2 transcription factor-like gene involved in stalk and leaf development in maize has been identified. Mutations near exon-intron junctions of the AP2 gene give mis-spliced transcript variants, which result in shorter internodes and wrinkled leaves

ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model

The advent of large language models, enabling flexibility through instruction-driven approaches, has revolutionized many traditional generative tasks, but large models for 3D data, particularly in comprehensively handling 3D shapes with other modalities, are still under-explored. By achieving instruction-based shape generations, versatile multimodal generative shape models can significantly benefit various fields like 3D virtual construction and network-aided design. In this work, we present ShapeGPT, a shape-included multi-modal framework to leverage strong pre-trained language models to address multiple shape-relevant tasks. Specifically, ShapeGPT employs a word-sentence-paragraph framework to discretize continuous shapes into shape words, further assembles these words for shape sentences, as well as integrates shape with instructional text for multi-modal paragraphs. To learn this shape-language model, we use a three-stage training scheme, including shape representation, multimodal alignment, and instruction-based generation, to align shape-language codebooks and learn the intricate correlations among these modalities. Extensive experiments demonstrate that ShapeGPT achieves comparable performance across shape-relevant tasks, including text-to-shape, shape-to-text, shape completion, and shape editing

Inhibition of Aurora B by CCT137690 sensitizes colorectal cells to radiotherapy

Colorectal cancer is the third most commonly diagnosed cancer worldwide. Although surgery remains the best treatment for this disease, adjuvant chemotherapy and radiotherapy are also very important in clinical practice. However, the notorious refractory lack of responses to radiochemotherapy greatly limits the application of radiochemotherapy in the context of colorectal cancer. There is a growing interest in the role that Aurora B may play in colorectal cancer cell survival as well as other cancer subtypes. In the current study, we sought to ascertain whether blocking of Aurora B signaling machinery by a small molecule inhibitor, CCT137690, could synergize radiation-induced colorectal cancer cell death. Results showed that CCT137690 increases the sensitivity of SW620 cells to radiation. Mechanistic studies revealed that Aurora B-Survivin pathway may be involved in this synergistic effect. Taken together, our results for the first time show that Aurora B inhibition and radiation exert a synergistic effect, resulting in enhanced colorectal cancer cell death. This synergistic effect is clinically relevant as lower doses of radiation could be used for cancer treatment, and could provide significant clinical benefits in terms of colorectal cancer management, while reducing unwanted side-effects

PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation

Recent advances in implicit neural representations have achieved impressive results by sampling and fusing individual points along sampling rays in the sampling space. However, due to the explosively growing sampling space, finely representing and synthesizing detailed textures remains a challenge for unbounded large-scale outdoor scenes. To alleviate the dilemma of using individual points to perceive the entire colossal space, we explore learning the surface distribution of the scene to provide structural priors and reduce the samplable space and propose a Point Diffusion implicit Function, PDF, for large-scale scene neural representation. The core of our method is a large-scale point cloud super-resolution diffusion module that enhances the sparse point cloud reconstructed from several training images into a dense point cloud as an explicit prior. Then in the rendering stage, only sampling points with prior points within the sampling radius are retained. That is, the sampling space is reduced from the unbounded space to the scene surface. Meanwhile, to fill in the background of the scene that cannot be provided by point clouds, the region sampling based on Mip-NeRF 360 is employed to model the background representation. Expensive experiments have demonstrated the effectiveness of our method for large-scale scene novel view synthesis, which outperforms relevant state-of-the-art baselines.Comment: Accepted to NeurIPS 202

The selenoproteome exhibits widely varying, tissue-specific dependence on selenoprotein P for selenium supply

Selenoprotein P (Sel P) is a selenium-rich glycoprotein believed to play a key role in selenium (Se) transport throughout the body. Development of a Sel P knockout mouse model has supported this notion and initial studies have indicated that selenium supply to various tissues is differentially affected by genetic deletion of Sel P. Se in the form of the amino acid, selenocysteine, is incorporated into selenoproteins at UGA codons. Thus, Se availability affects not only selenoprotein levels, but also the turnover of selenoprotein mRNAs via the nonsense-mediated decay pathway. We investigated how genetic deletion of Sel P in mice affected levels of the mRNAs encoding all known members of the murine selenoprotein family, as well as three non-selenoprotein factors involved in their synthesis, selenophosphate synthetase 1 (SPS1), SECIS-binding protein 2 (SBP2) and SECp43. Our findings present a comprehensive description of selenoprotein mRNA expression in the following murine tissues: brain, heart, intestine, kidney, liver, lung, spleen and testes. We also describe how abundance of selenoproteins and selenoprotein-synthesis factors are affected by genetic deletion of Sel P in some of these tissues, providing insight into how the presence of this selenoprotein influences selenoprotein mRNA levels, and thus, the selenoproteome