A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Easy and Efficient Transformer : Scalable Inference Solution For large NLP model

Recently, large-scale transformer-based models have been proven to be effective over a variety of tasks across many domains. Nevertheless, putting them into production is very expensive, requiring comprehensive optimization techniques to reduce inference costs. This paper introduces a series of transformer inference optimization techniques that are both in algorithm level and hardware level. These techniques include a pre-padding decoding mechanism that improves token parallelism for text generation, and highly optimized kernels designed for very long input length and large hidden size. On this basis, we propose a transformer inference acceleration library -- Easy and Efficient Transformer (EET), which has a significant performance improvement over existing libraries. Compared to Faster Transformer v4.0's implementation for GPT-2 layer on A100, EET achieves a 1.5-4.5x state-of-art speedup varying with different context lengths. EET is available at https://github.com/NetEase-FuXi/EET. A demo video is available at https://youtu.be/22UPcNGcErg

Observation of Viruses, Bacteria, and Fungi in Clinical Skin Samples under Transmission Electron Microscopy

The highlight of this chapter is the description of the clinical manifestation and its pathogen and the host tissue damage observed under the transmission electron microscopy, which helps the clinician understand the pathogen’s ultrastructure, the change of host sub-cell structure, and helps the laboratory workers understand the pathogen-induced human skin lesions’ clinical characteristics, to establish a two-way learning exchange database with vivid images

Identifying Examinees Who Possess Distinct and Reliable Subscores When Added Value is Lacking for the Total Sample

Research has demonstrated that although subdomain information may provide no added value beyond the total score, in some contexts such information is of utility to particular demographic subgroups (Sinharay & Haberman, 2014). However, it is argued that the utility of reporting subscores for an individual should not be based on one’s manifest characteristics (e.g., gender or ethnicity), but rather on individual needs for diagnostic information, which is driven by multidimensionality in subdomain scores. To improve the validity of diagnostic information, this study proposed the use of Mahalanobis Distance and HT indices to assess whether an individual’s data significantly departs from unidimensionality. Those examinees that were found to differ significantly were then assessed separately for subscore added value via Haberman’s (2008) procedure. To this end, simulation analyses were conducted to evaluate Type I error, power, and recovery of subscore added value classifications for various levels of subdomain test lengths, subdomain inter-correlations, and proportions of multidimensionality in the total sample. Results demonstrated that the HT index possessed around 100% power across all conditions, while maintaining Type I error below 5%, which led to nearly perfect recovery of subscore added value classifications. In contrast, the power rates for Mahalanobis Distance were much lower ranging from 13% to 61% with Type I errors maintained at the nominal level of 5%. Although the power rates were below the desired criterion of 80%, the cases identified as aberrant using this method were found to have greater variability between subdomain scores, increased reliability, and lower observed subdomain correlations when compared to the generated data. As a result, outlier cases were found to have subscore added value for nearly 100% of cases across conditions even when the generated multidimensional data did not possess subscore added value. These results were cross-validated using a large-scale high-stakes test in which the Mahalanobis Distance measure was found to identify 6.57% of 8,803 test-takers that possessed subscores with added-value who otherwise would have been masked by the unidimensionality of the total sample. Overall, this study suggests that the Mahalanobis Distance measure shows some promise in identifying examinees with multidimensional score profiles

Application of Angiotensin Receptor–Neprilysin Inhibitor in Chronic Kidney Disease Patients: Chinese Expert Consensus

Chronic kidney disease (CKD) is a global public health problem, and cardiovascular disease is the most common cause of death in patients with CKD. The incidence and prevalence of cardiovascular events during the early stages of CKD increases significantly with a decline in renal function. More than 50% of dialysis patients die from cardiovascular disease, including coronary heart disease, heart failure, arrhythmia, and sudden cardiac death. Therefore, developing effective methods to control risk factors and improve prognosis is the primary focus during the diagnosis and treatment of CKD. For example, the SPRINT study demonstrated that CKD drugs are effective in reducing cardiovascular and cerebrovascular events by controlling blood pressure. Uncontrolled blood pressure not only increases the risk of these events but also accelerates the progression of CKD. A co-crystal complex of sacubitril, which is a neprilysin inhibitor, and valsartan, which is an angiotensin receptor blockade, has the potential to be widely used against CKD. Sacubitril inhibits neprilysin, which further reduces the degradation of natriuretic peptides and enhances the beneficial effects of the natriuretic peptide system. In contrast, valsartan alone can block the angiotensin II-1 (AT1) receptor and therefore inhibit the renin–angiotensin–aldosterone system. These two components can act synergistically to relax blood vessels, prevent and reverse cardiovascular remodeling, and promote natriuresis. Recent studies have repeatedly confirmed that the first and so far the only angiotensin receptor–neprilysin inhibitor (ARNI) sacubitril/valsartan can reduce blood pressure more effectively than renin–angiotensin system inhibitors and improve the prognosis of heart failure in patients with CKD. Here, we propose clinical recommendations based on an expert consensus to guide ARNI-based therapeutics and reduce the occurrence of cardiovascular events in patients with CKD

Observational Constraints on the Oxidation of NO_x in the Upper Troposphere

NO_x (NO_x ≡ NO + NO_2) regulates O_3 and HO_x (HO_x ≡ OH + HO_2) concentrations in the upper troposphere. In the laboratory, it is difficult to measure rates and branching ratios of the chemical reactions affecting NO_x at the low temperatures and pressures characteristic of the upper troposphere, making direct measurements in the atmosphere especially useful. We report quasi-Lagrangian observations of the chemical evolution of an air parcel following a lightning event that results in high NO_x concentrations. These quasi-Lagrangian measurements obtained during the Deep Convective Clouds and Chemistry experiment are used to characterize the daytime rates for conversion of NOx to different peroxy nitrates, the sum of alkyl and multifunctional nitrates, and HNO_3. We infer the following production rate constants [in (cm^3/molecule)/s] at 225 K and 230 hPa: 7.2(±5.7) × 10^(–12) (CH_3O_2NO_2), 5.1(±3.1) × 10^(–13) (HO_2NO_2), 1.3(±0.8) × 10^(–11) (PAN), 7.3(±3.4) × 10^(–12) (PPN), and 6.2(±2.9) × 10^(–12) (HNO_3). The HNO_3 and HO_2NO_2 rates are ∼30–50% lower than currently recommended whereas the other rates are consistent with current recommendations to within ±30%. The analysis indicates that HNO_3 production from the HO_2 and NO reaction (if any) must be accompanied by a slower rate for the reaction of OH with NO_2, keeping the total combined rate for the two processes at the rate reported for HNO_3 production above

Economic and environmental impacts of agricultural non-tariff measures: evidence based on ad valorem equivalent estimates

Non-tariff measures as hidden barriers to agricultural trade would not only result in production and welfare distortions due to the international relocation of activities along the agricultural value chain, but also yield subsequent consequences to both the scale and distribution of carbon emissions from the agri-food system. This paper estimates ad valorem equivalents of non-tariff measures using a gravity model in combination with detailed bilateral trade data of 2001-2019, and incorporates the estimations in the Global Trade Analysis Project model and a multi-regional input-output table of Eora26 to quantify economic and environmental impacts of non-tariff measures. We show that while tariff equivalents are on average positive for all types of non-tariff measures, there are substantial heterogeneities across countries and products. The extra trade barriers imposed by these measures would increase the scale of domestic agriculture-related sectors for most agriculture importing countries, and vice versa for major exporters. Meanwhile, they would reduce the global welfare at amount of 16 millions US dollars on average and in particular, the welfare of key imposers and targeting markets of non-tariff measures. Carbon emissions from the agri-food system tend to increase about 1% around the world, especially due to the larger food processing industry in developed countries. Our paper confirms that non-tariff measures lead to both welfare distortions and carbon emissions in the agri-food system. It thus calls for urgent needs to promote further reforms of the agricultural trade regime and the policy coordination across countries to facilitate agri-food system transformation with more integration and sustainability