InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.Comment: 5 pages, 4 figure

GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin.Comment: 14 page

GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.Comment: 13 page

Baseline Demographic and Clinical Characteristics of Patients with Adrenal Incidentaloma from a Single Center in China: A Survey

Aim. To investigate the clinical and endocrinological characteristics of patients with adrenal incidentaloma (AI). Materials and Methods. This retrospective study enrolled 1941 AI patients hospitalized at the Department of Endocrinology, Chinese PLA General Hospital, Beijing, China, between January 1997 and December 2016. The patient gender, age at visits, imaging features, functional status, and histological results were analyzed. Results. Of the 1941 patients, 984 (50.70%) were men. The median age was 52 years (interquartile range: 44–69 years). 140 cases had bilateral AI. Endocrine evaluation showed that 1411 (72.69%) patients had nonfunctional tumor, 152 (7.83%) had subclinical Cushing syndrome (SCS), and 82 (4.33%) had primary hyperaldosteronism. A total of 925 patients underwent operation for removal of 496 cortical adenomas (53.62%), 15 adrenal cortical carcinomas (1.62%), and 172 pheochromocytomas (18.59%). The bilateral group had a higher proportion of SCS (18.57% versus 7.10%, P<0.001, P=0.006). A mass size of 46 mm was of great value in distinguishing malignant tumors from the benign tumors, with sensitivity of 88.2% and specificity of 95.5%. Conclusions. We reported the baseline demographic and clinical characteristics of patients with AI in a large series from a single center in China

Neutron generation enhanced by a femtosecond laser irradiating on multi-channel target

A novel scheme has been proposed to enhance neutron yields, in which a multi-channel target consisting of a row of parallel micro-wires and a plane substrate is irradiated by a relativistic femtosecond laser. Two-dimensional particle-in-cell simulations show that the multi-channel target can significantly enhance the neutron yield, which is about 4 orders of magnitude greater than the plane target. Different from the case of nanowire target, we find that when the laser penetrates into the channel, the excited transverse sheath electric field can effectively accelerate the D+ ions in the transverse direction. When these energetic D+ ions move towards the nearby wire, they will collide with the bulk D+ ions to trigger D-D fusion reaction and produce neutrons, which is much more effective than the plane target case. Due to the unique trajectory of the incident D+ ions, the angular distribution of the produced neutrons is modulated from isotropic to two peaks around ±90°. Meanwhile, this enhancement and modulation is further verified in a wide range of target parameters

Comprehensive transcriptional profiling of aging porcine liver

Background Aging is a major risk factor for the development of many diseases, and the liver, as the most important metabolic organ, is significantly affected by aging. It has been shown that the liver weight tends to increase in rodents and decrease in humans with age. Pigs have a genomic structure, with physiological as well as biochemical features that are similar to those of humans, and have therefore been used as a valuable model for studying human diseases. The molecular mechanisms of the liver aging of large mammals on a comprehensive transcriptional level remain poorly understood. The pig is an ideal model animal to clearly and fully understand the molecular mechanism underlying human liver aging. Methods In this study, four healthy female Yana pigs (an indigenous Chinese breed) were investigated: two young sows (180-days-old) and two old sows (8-years-old). High throughput RNA sequencing was performed to evaluate the expression profiles of messenger RNA, long non-coding RNAs, micro RNAs, and circular RNAs during the porcine liver aging process. Gene Ontology (GO) analysis was performed to investigate the biological functions of age-related genes. Results A number of age-related genes were identified in the porcine liver. GO annotation showed that up-regulated genes were mainly related to immune response, while the down-regulated genes were mainly related to metabolism. Moreover, several lncRNAs and their target genes were also found to be differentially expressed during liver aging. In addition, the multi-group cooperative control relationships and constructed circRNA-miRNA co-expression networks were assessed during liver aging. Conclusions Numerous age-related genes were identified and circRNA-miRNA co-expression networks that are active during porcine liver aging were constructed. These findings contribute to the understanding of the transcriptional foundations of liver aging and also provide further references that clarify human liver aging at the molecular level

dbDEPC 2.0: updated database of differentially expressed proteins in human cancers

A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do