A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers

Solvent Isotope Effect on Hydrogen-Transfer Reduction of CO2 into Formate with Glycerine by Alkaline Hydrothermal Reaction

To examine the solvent isotope effect on hydrogen-transfer reduction of CO2 into formate with glycerine by alkaline hydrothermal reaction, intermediates were identified by 13C-NMR, 1H-NMR, 2H-NMR, LC-MS analyses. The results showed that (1) CO2 was indeed converted into abiogenic formate; (2) a ketone carbonyl group as intermediate product was formed on hydrogen-transfer reduction of CO2 into formate with glycerine by alkaline hydrothermal reaction; (3) acetol was the most probable intermediate in the first reaction by undergoing a dehydration rather than a dehydrogenation

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers

Antibiotic therapy and risk of early-onset colorectal cancer: A national case-control study

INTRODUCTION: Antibiotic use has emerged as a risk factor for colorectal neoplasia and is hypothesized as a contributor to the rising incidence of colorectal cancer under age 50 years or early-onset colorectal cancer (EOCRC). However, the impact of antibiotic use and risk of EOCRC is unknown. METHODS: We conducted a population-based case-control study of CRC among individuals aged ≥18 years in the Epidemiology Strengthened by histoPathology Reports in Sweden (ESPRESSO) cohort (2006-2016). The primary outcome was EOCRC. A secondary outcome was CRC at any age. Incident CRC was pathologically confirmed, and for each, up to 5 population-based controls were matched on age, sex, county of residence, and calendar year. We assessed prescriptions until 6 months before CRC diagnosis. Conditional logistic regression was used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). RESULTS: We identified 54,804 cases of CRC (2,557 EOCRCs) and 261,089 controls. Compared with none, previous antibiotic use was not associated with EOCRC risk after adjustment for potential confounders (aOR 1.06, 95% CI: 0.96, 1.17) with similarly null findings when stratified by anatomic tumor site. In contrast, previous antibiotic use was weakly associated with elevated risk for CRC at any age (aOR 1.05, 95% CI: 1.02, 1.07). A potential but modest link between broad-spectrum antibiotic use and EOCRC was observed (aOR 1.13, 95% CI: 1.02, 1.26). DISCUSSION: We found no conclusive evidence that antibiotics are associated with EOCRC risk. Although antibiotic use was weakly associated with risk of CRC at any age, the magnitude of association was modest, and the study period was relatively short

Lexical data augmentation for sentiment analysis

Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part-of-speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic-based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution-based data augmentation methods, one of which is thesaurus-based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST-2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM-AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well-designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms

Modulation of Gut Microbiota by Low Methoxyl Pectin Attenuates Type 1 Diabetes in Non-obese Diabetic Mice

Intestinal homeostasis underpins the development of type 1 diabetes (T1D), and dietary manipulations to enhance intestinal homeostasis have been proposed to prevent T1D. The current study aimed to investigate the efficacy of supplementing a novel specific low-methoxyl pectin (LMP) dietary fiber in preventing T1D development. Female NOD mice were weaned onto control or 5% (wt/wt) LMP supplemented diets for up to 40 weeks of age, overt diabetes incidence and blood glucose were monitored. Then broad-spectrum antibiotics (ABX) treatment per os for 7 days followed by gut microbiota transfer was performed to demonstrate gut microbiota-dependent effects. Next-generation sequencing was used for analyzing the composition of microbiota in caecum. Concentration of short chain fatty acids were determined by GC-MS. The barrier reinforcing tight junction proteins zonula occludens-2 (ZO-2), claudin-1 and NOD like receptor protein 3 (NLRP3) inflammasome activation were determined by Western blot. The proportion of CD25(+)Foxp3(+)CD4(+) regulatory T cell (Foxp3(+) Treg) in the pancreas, pancreatic and mesenteric lymph nodes was analyzed by flow cytometry. We found that LMP supplementation ameliorated T1D development in non-obese diabetic (NOD) mice, as evidenced by decreasing diabetes incidence and fasting glucose levels in LMP fed NOD mice. Further microbiota analysis revealed that LMP supplementation prevented T1D-associated caecal dysbiosis and selectively enriched caecal bacterial species to produce more SCFAs. The LMP-mediated microbial balance further enhanced caecal barrier function and shaped gut-pancreatic immune environment, as characterized by higher expression of tight junction proteins claudin-1, ZO-2 in caecum, increased Foxp3(+) Treg population and decreased NLRP3 inflammasome activation in both caecum and pancreas. The microbiota-dependent beneficial effect of LMP on T1D was further proven by the fact that aberration of caecal microbiota by ABX treatment worsened T1D autoimmunity and could be restored with transfer of feces of LMP-fed NOD mice. These data demonstrate that this novel LMP limits T1D development by inducing caecal homeostasis to shape pancreatic immune environment. This finding opens a realistic option for gut microbiota manipulation and prevention of T1D in humans

The Sulfur Microbial Diet Is Associated With Increased Risk of Early-Onset Colorectal Cancer Precursors

Background & Aims: Diet may contribute to the increasing incidence of colorectal cancer (CRC) before age 50 (early-onset CRC). Microbial metabolism of dietary sulfur produces hydrogen sulfide (H2S), a gastrointestinal carcinogen that cannot be easily measured at scale. As a result, evidence supporting its role in early neoplasia is lacking. Methods: We evaluated long-term adherence to the sulfur microbial diet, a dietary index defined a priori based on increased abundance of 43 bacterial species involved with sulfur metabolism, with risk of CRC precursors among 59,013 individuals who underwent lower endoscopy in the Nurses’ Health Study II (1991–2015), a prospective cohort study with dietary assessment every 4 years through validated food frequency questionnaires and an assessment of dietary intake during adolescence in 1998. The sulfur microbial diet was characterized by intake high in processed meats, foods previously linked to CRC development, and low in mixed vegetables and legumes. Multivariable logistic regression for clustered data was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Results: We documented 2911 cases of early-onset adenoma. After adjusting for established risk factors, higher sulfur microbial diet scores were associated with increased risk for early-onset adenomas (ORquartile [Q]4 vs Q1, 1.31; 95% CI, 1.10–1.56, Ptrend = .02), but not serrated lesions. Compared with the lowest, women in the highest quartile of sulfur microbial diet scores had significantly increased risk of early-onset adenomas with greater malignant potential (ORQ4 vs Q1, 1.65 for villous/tubulovillous histology; 95% CI, 1.12–2.43; Ptrend = .04). Similar trends for early-onset adenoma were observed based on diet consumed during adolescence. In contrast, no clear association for adenomas was identified after age 50. Conclusions: Our findings in a cohort of young women support a role for dietary interactions with gut sulfur-metabolizing bacteria in early-onset colorectal carcinogenesis, possibly beginning in adolescence. Includes Supplemental materials