CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources: 1) a slot-based dialog (SBD) dataset with table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed knowledge. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing. The proposed experimental settings include the combinations of training with either the entire training set or a few-shot training set, and testing with either the standard test set or a hard test subset, which can assess model capabilities in terms of general prediction, fast adaptability and reliable robustness.Comment: EMNLP 202

Research on Generalized Intelligent Routing Technology Based on Graph Neural Network

Aiming at the problems of poor load balancing ability and weak generalization of the existing routing algorithms, this paper proposes an intelligent routing algorithm, GNN-DRL, in the Software Defined Networking (SDN) environment. The GNN-DRL algorithm uses a graph neural network (GNN) to perceive the dynamically changing network topology, generalizes the state of nodes and edges, and combines the self-learning ability of Deep Reinforcement Learning (DRL) to find the optimal routing strategy, which makes GNN-DRL minimize the maximum link utilization and reduces average end-to-end delay under high network load. In this paper, the GNN-DRL intelligent routing algorithm is compared with the Open Shortest Path First (OSPF), Equal-Cost Multi-Path (ECMP), and intelligence-driven experiential network architecture for automatic routing (EARS). The experimental results show that GNN-DRL reduces the maximum link utilization by 13.92% and end-to-end delay by 9.48% compared with the superior intelligent routing algorithm EARS under high traffic load, and can be effectively extended to different network topologies, making possible better load balancing capability and generalizability

Research on Generalized Intelligent Routing Technology Based on Graph Neural Network

Aiming at the problems of poor load balancing ability and weak generalization of the existing routing algorithms, this paper proposes an intelligent routing algorithm, GNN-DRL, in the Software Defined Networking (SDN) environment. The GNN-DRL algorithm uses a graph neural network (GNN) to perceive the dynamically changing network topology, generalizes the state of nodes and edges, and combines the self-learning ability of Deep Reinforcement Learning (DRL) to find the optimal routing strategy, which makes GNN-DRL minimize the maximum link utilization and reduces average end-to-end delay under high network load. In this paper, the GNN-DRL intelligent routing algorithm is compared with the Open Shortest Path First (OSPF), Equal-Cost Multi-Path (ECMP), and intelligence-driven experiential network architecture for automatic routing (EARS). The experimental results show that GNN-DRL reduces the maximum link utilization by 13.92% and end-to-end delay by 9.48% compared with the superior intelligent routing algorithm EARS under high traffic load, and can be effectively extended to different network topologies, making possible better load balancing capability and generalizability

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

With the vigorous development of the Internet, the network traffic of data centers has exploded, and at the same time, the network energy consumption of data centers has also increased rapidly. Existing routing algorithms only realize routing optimization through Quality of Service (QoS) and Quality of Experience (QoE), which ignores the energy consumption of data center networks. Aiming at this problem, this paper proposes an Ee-Routing algorithm, which is an energy-saving routing algorithm based on deep reinforcement learning. First, our method takes the energy consumption and network performance of the data plane in the software-defined network as the joint optimization goal and establishes an energy-efficient traffic scheduling scheme for the elephant flows and the mice flows. Then, we use Deep Deterministic Policy Gradient (DDPG), which is a deep learning framework, to achieve continuous and energy-efficient traffic scheduling for joint optimization goals. The training process of our method is based on a Convolutional Neural Network (CNN), which can effectively improve the convergence efficiency of the algorithm. After the algorithm training converges, the energy-efficient path weights of the elephant flows and the mice flows are output, and the balanced scheduling of routing energy-saving and network performance is completed. Finally, the results show that our algorithm has good convergence and stability. Compared with the DQN-EER routing algorithm, Ee-Routing improves the energy saving percentage by 13.93%, and compared with the EARS routing algorithm, Ee-Routing reduces the delay by 13.73%, increases the throughput by 10.91%, and reduces the packet loss rate by 13.51%

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

With the vigorous development of the Internet, the network traffic of data centers has exploded, and at the same time, the network energy consumption of data centers has also increased rapidly. Existing routing algorithms only realize routing optimization through Quality of Service (QoS) and Quality of Experience (QoE), which ignores the energy consumption of data center networks. Aiming at this problem, this paper proposes an Ee-Routing algorithm, which is an energy-saving routing algorithm based on deep reinforcement learning. First, our method takes the energy consumption and network performance of the data plane in the software-defined network as the joint optimization goal and establishes an energy-efficient traffic scheduling scheme for the elephant flows and the mice flows. Then, we use Deep Deterministic Policy Gradient (DDPG), which is a deep learning framework, to achieve continuous and energy-efficient traffic scheduling for joint optimization goals. The training process of our method is based on a Convolutional Neural Network (CNN), which can effectively improve the convergence efficiency of the algorithm. After the algorithm training converges, the energy-efficient path weights of the elephant flows and the mice flows are output, and the balanced scheduling of routing energy-saving and network performance is completed. Finally, the results show that our algorithm has good convergence and stability. Compared with the DQN-EER routing algorithm, Ee-Routing improves the energy saving percentage by 13.93%, and compared with the EARS routing algorithm, Ee-Routing reduces the delay by 13.73%, increases the throughput by 10.91%, and reduces the packet loss rate by 13.51%

Table_1_Individual-level socioeconomic status and cataract-induced visual disability among older adults in China: the overview and urban-rural difference.DOCX

ObjectiveTo investigate the prevalence of cataract-induced visual disability and its association with individual-level socioeconomic status (SES) among older adults in China.MethodsUsing the data of 354,743 older adults (60 years and older) from the Second China National Sample Survey on Disability in 2006. Cross-sectional study design was applied. The differences in visual disability prevalence of cataracts among sociodemographic subgroups were analyzed by the chi-square test, and the association between individual-level SES and cataract-induced visual disability was investigated by the multivariate logistic regression model.ResultsThe weighted visual disability prevalence of cataracts was 4.84% in 2006. Older people with a higher household income per capita (OR = 0.83, 95% CI: 0.81–0.85), higher education level (primary school vs. illiteracy: OR = 0.80, 95% CI: 0.76–0.83; ≥undergraduate college vs. illiteracy: OR = 0.31, 95% CI: 0.25–0.39), and occupation (OR = 0.53, 95% CI: 0.50–0.56) were less likely to suffer from cataract-induced visual disability. Household income per capita and education level increase played a greater role in decreasing the risk of visual disability caused by cataracts in urban areas, while having occupation contributed more to reducing the risk of disability in rural areas.ConclusionThe gap in individual-level SES is closely related to the visual health inequities among older Chinese people and there are two distinct mechanisms in rural and urban areas. Strategies to promote collaborative healthcare development regionally, strengthen safeguards for disadvantaged groups, and increase public awareness of visual disability prevention are warranted.</p

Table 1_Individual-level socioeconomic status and cataract-induced visual disability among older adults in China: the overview and urban-rural difference.DOCX

ObjectiveTo investigate the prevalence of cataract-induced visual disability and its association with individual-level socioeconomic status (SES) among older adults in China.MethodsUsing the data of 354,743 older adults (60 years and older) from the Second China National Sample Survey on Disability in 2006. Cross-sectional study design was applied. The differences in visual disability prevalence of cataracts among sociodemographic subgroups were analyzed by the chi-square test, and the association between individual-level SES and cataract-induced visual disability was investigated by the multivariate logistic regression model.ResultsThe weighted visual disability prevalence of cataracts was 4.84% in 2006. Older people with a higher household income per capita (OR = 0.83, 95% CI: 0.81–0.85), higher education level (primary school vs. illiteracy: OR = 0.80, 95% CI: 0.76–0.83; ≥undergraduate college vs. illiteracy: OR = 0.31, 95% CI: 0.25–0.39), and occupation (OR = 0.53, 95% CI: 0.50–0.56) were less likely to suffer from cataract-induced visual disability. Household income per capita and education level increase played a greater role in decreasing the risk of visual disability caused by cataracts in urban areas, while having occupation contributed more to reducing the risk of disability in rural areas.ConclusionThe gap in individual-level SES is closely related to the visual health inequities among older Chinese people and there are two distinct mechanisms in rural and urban areas. Strategies to promote collaborative healthcare development regionally, strengthen safeguards for disadvantaged groups, and increase public awareness of visual disability prevention are warranted.</p

GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings. For reproducibility, we release the code and data at https://github.com/siat-nlp/GALAXY

CPT1C-mediated fatty acid oxidation facilitates colorectal cancer cell proliferation and metastasis

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells