Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.Comment: COLING 202

Scenario Forecasting for Global Tourism

This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting

A General Quantitative Genetic Model for Haplotyping a Complex Trait in Humans

Uncertainty about linkage phases of multiple single nucleotide polymorphisms (SNPs) in heterozygous diploids challenges the identification of specific DNA sequence variants that encode a complex trait. A statistical technique implemented with the EM algorithm has been developed to infer the effects of SNP haplotypes from genotypic data by assuming that one haplotype (called the risk haplotype) performs differently from the rest (called the non-risk haplotype). This assumption simplifies the definition and estimation of genotypic values of diplotypes for a complex trait, but will reduce the power to detect the risk haplotype when non-risk haplotypes contain substantial diversity. In this article, we incorporate general quantitative genetic theory to specify the differentiation of different haplotypes in terms of their genetic control of a complex trait. A model selection procedure is deployed to test the best number and combination of risk haplotypes, thus providing a precise and powerful test of genetic determination in association studies. Our method is derived on the maximum likelihood theory and has been shown through simulation studies to be powerful for the characterization of the genetic architecture of complex quantitative traits

Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping

Robotic grasping has always been a challenging task for both service and industrial robots. The ability of grasp planning for novel objects is necessary for a robot to autonomously perform grasps under unknown environments.In this work, we consider the task of grasp planning for a parallel gripper to grasp a novel object, given an RGB image and its corresponding depth image taken from a single view. In this paper, we show that this problem can be simplified by modeling a novel object as a set of simple shape primitives, such as ellipses. We adopt fuzzy Gaussian mixture models (GMMs) for novel objects’ shape approximation. With the obtained GMM, we decompose the object into several ellipses, while each ellipse is corresponding to a grasping rectangle. After comparing the grasp quality among these rectangles, we will obtain the most proper part for a gripper to grasp. Extensive experiments on a real robotic platform demonstrate that our algorithm assists the robot to grasp a variety of novel objects with good grasp quality and computational efficiency

Depletion of pre-mRNA splicing factor Cdc5L inhibits mitotic progression and triggers mitotic catastrophe.

Disturbing mitotic progression via targeted anti-mitotic therapy is an attractive strategy for cancer treatment. Therefore, the exploration and elucidation of molecular targets and pathways in mitosis are critical for the development of anti-mitotic drugs. Here, we show that cell division cycle 5-like (Cdc5L), a pre-mRNA splicing factor, is a regulator of mitotic progression. Depletion of Cdc5L causes dramatic mitotic arrest, chromosome misalignments and sustained activation of spindle assembly checkpoint, eventually leading to mitotic catastrophe. Moreover, these defects result from severe impairment of kinetochore-microtubule attachment and serious DNA damage. Genome-wide gene expression analysis reveals that Cdc5L modulates the expression of a set of genes involved in the mitosis and the DNA damage response. We further found that the pre-mRNA splicing efficiency of these genes were impaired when Cdc5L was knocked down. Interestingly, Cdc5L is highly expressed in cervical tumors and osteosarcoma. Finally, we demonstrate that downregulation of Cdc5L decreases the cell viability of related tumor cells. These results suggest that Cdc5L is a key regulator of mitotic progression and highlight the potential of Cdc5L as a target for cancer therapy

Label-Based Disentanglement Measure among Hidden Units of Deep Learning

The capability to disentangle underlying factors hidden in the observable data, thereby obtaining their abstract representations, is considered one important ingredient for the subsequent success of deep networks in various application scenarios. Recently, numerous practical measures and learning strategies have been established for disentanglement, showcasing their potential in improving the model’s explainability, controlability, and robustness. However, when the downstream tasks come to the classification issues, there is still no consensus in the community on the definition or measurement for disentanglement, and its connection to the generalization capacity remains not very clear. Aiming at this, we explore the highly non-linear effect of a specified hidden layer on the generalization capacity from an information perspective and obtain a tight bound. Upon decompsing the bound, we find that besides the unsupervised disentanglement measure term in the conventional sense, a new supervised disentanglement term also emerges with a nonnegligible effect on the generality. Consequently, a novel label-based disentanglement measure (LDM) is naturally introduced as the discrepancy between these two terms under the supervised learning settings to substitute the commonly used unsupervised disentanglement measure. The theoretical analysis reveals an inverse relationship between the defined LDM and the generalization capacity. Finally, using LDM as regularizer, the experiments show that the deep neural networks (DNNs) can effectively reduce generalization error while improving classification accuracy when noise is added to the data features or labels, which strongly supports our points

Study on performance degradation and damage modes of thin-film photovoltaic cell subjected to particle impact

It has been a key issue for photovoltaic (PV) cells to survive under mechanical impacts by tiny dust. In this paper, the performance degradation and the damage behavior of PV cells subjected to massive dust impact are investigated using laser-shock driven particle impact experiments and mechanical modeling. The results show that the light-electricity conversion efficiency of the PV cells decreases with increasing the impact velocity and the particles’ number density. It drops from 26.7 to 3.9% with increasing the impact velocity from 40 to 185 m/s and the particles’ number densities from 35 to 150/mm², showing a reduction up to 85.7% when being compared with the intact ones with the light-electricity conversion efficiency of 27.2%. A damage-induced conversion efficiency degradation (DCED) model is developed and validated by experiments, providing an effective method in predicting the performance degradation of PV cells under various dust impact conditions. Moreover, three damage modes, including damaged conducting grid lines, fractured PV cell surfaces, and the bending effects after impact are observed, and the corresponding strength of each mode is quantified by different mechanical theories

Prenatal diagnosis of midgut volvulus by fetal MRI: a retrospective study

BackgroundFetal midgut volvulus is a rare disease, with a high risk of potentially life-threatening fetal complications.PurposeThe aim of this study was to retrospectively analyze the imaging findings of fetal midgut volvulus diagnosed by magnetic resonance imaging (MRI) and explore its value in non-invasive prenatal diagnosis.MethodsA retrospective collection of data from 156 fetuses suspected of intestinal obstruction by ultrasound examination in our hospital was conducted. All ultrasound examinations showed fetal intestinal dilation and fetal MRI diagnosis suspected midgut volvulus in 32 cases (32/156), of which 18 cases (18/32) that underwent surgical treatment in the neonatal period were confirmed to have midgut volvulus. MRI signs in the 18 fetuses with midgut volvulus were analyzed.ResultsDuring MRI examination, all 18 fetuses showed gastric and/or intestinal dilatation, most of which showed different degrees of obstruction in T1-weighted images (WIs) and T2WIs, showing the “black and white sign” (14/18), “whirlpool sign” (10/18), and “coffee bean sign” (6/18). High signal intensity changes in diffusion-weighted imaging sequences were observed in intestinal tubes with ischemia and infarction. Direct signs of vascular torsion were observed in some cases (8/18). MRI signs indicated fetal midgut volvulus with hydramnios (16/18), meconium pseudocyst (7/18), meconium peritonitis (4/18), testicular hydrocele (3/18), and secondary pulmonary dysplasia (6/18). Operations confirmed the diagnosis of segmental midgut volvulus in 15 cases, complete midgut volvulus in 3 cases, and combined with intestinal atresia in 8 cases.ConclusionPrenatal MRI plays an important role in the diagnosis of fetal midgut volvulus and the discovery of its complications, which can guide the treatment after birth and provide a reference for the prognosis