Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages

This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker), and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training. Extensive experiment results demonstrate the effectiveness of our model and we achieved 4th place in the DialDoc 2023 Competition. Therefore, CLEM can serve as a solution to resource scarcity in DGDS and provide useful guidance for multi-lingual alignment tasks

Adaptive Resource Allocation for Workflow Containerization on Kubernetes

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named ARAS for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod's lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in CPU and memory resource usage rate

Improving Question Generation with Multi-level Content Planning

This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context. To mitigate this issue, we propose MultiFactor, a novel QG framework based on multi-level content planning. Specifically, MultiFactor includes two components: FA-model, which simultaneously selects key phrases and generates full answers, and Q-model which takes the generated full answer as an additional input to generate questions. Here, full answer generation is introduced to connect the short answer with the selected key phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model and Q-model are formalized as simple-yet-effective Phrase-Enhanced Transformers, our joint model for phrase selection and text generation. Experimental results show that our method outperforms strong baselines on two popular QG datasets. Our code is available at https://github.com/zeaver/MultiFactor.Comment: Camera-ready. Accepted by EMNLP 2023 Finding

Diversify Question Generation with Retrieval-Augmented Style Transfer

Given a textual passage and an answer, humans are able to ask questions with various expressions, but this ability is still challenging for most question generation (QG) systems. Existing solutions mainly focus on the internal knowledge within the given passage or the semantic word space for diverse content planning. These methods, however, have not considered the potential of external knowledge for expression diversity. To bridge this gap, we propose RAST, a framework for Retrieval-Augmented Style Transfer, where the objective is to utilize the style of diverse templates for question generation. For training RAST, we develop a novel Reinforcement Learning (RL) based approach that maximizes a weighted combination of diversity reward and consistency reward. Here, the consistency reward is computed by a Question-Answering (QA) model, whereas the diversity reward measures how much the final output mimics the retrieved template. Experimental results show that our method outperforms previous diversity-driven baselines on diversity while being comparable in terms of consistency scores. Our code is available at https://github.com/gouqi666/RAST.Comment: EMNLP2023 camera-read

Multifunctional Biomimetic Nanovaccines Based on Photothermal and Weak-Immunostimulatory Nanoparticulate Cores for the Immunotherapy of Solid Tumors

An alternative strategy of choosing photothermal and weak-immunostimulatory porous silicon@Au nanocomposites as particulate cores to prepare a biomimetic nanovaccine is reported to improve its biosafety and immunotherapeutic efficacy for solid tumors. A quantitative analysis method is used to calculate the loading amount of cancer cell membranes onto porous silicon@Au nanocomposites. Assisted with foreign-body responses, these exogenous nanoparticulate cores with weak immunostimulatory effect can still efficiently deliver cancer cell membranes into dendritic cells to activate them and the downstream antitumor immunity, resulting in no occurrence of solid tumors and the survival of all immunized mice during 55 day observation. In addition, this nanovaccine, as a photothermal therapeutic agent, synergized with additional immunotherapies can significantly inhibit the growth and metastasis of established solid tumors, via the initiation of the antitumor immune responses in the body and the reversion of their immunosuppressive microenvironments. Considering the versatile surface engineering of porous silicon nanoparticles, the strategy developed here is beneficial to construct multifunctional nanovaccines with better biosafety and more diagnosis or therapeutic modalities against the occurrence, recurrence, or metastasis of solid tumors in future clinical practice.Peer reviewe

Multifunctional Biomimetic Nanovaccines Based on Photothermal and Weak-Immunostimulatory Nanoparticulate Cores for the Immunotherapy of Solid Tumors

An alternative strategy of choosing photothermal and weak-immunostimulatory porous silicon@Au nanocomposites as particulate cores to prepare a biomimetic nanovaccine is reported to improve its biosafety and immunotherapeutic efficacy for solid tumors. A quantitative analysis method is used to calculate the loading amount of cancer cell membranes onto porous silicon@Au nanocomposites. Assisted with foreign-body responses, these exogenous nanoparticulate cores with weak immunostimulatory effect can still efficiently deliver cancer cell membranes into dendritic cells to activate them and the downstream antitumor immunity, resulting in no occurrence of solid tumors and the survival of all immunized mice during 55 day observation. In addition, this nanovaccine, as a photothermal therapeutic agent, synergized with additional immunotherapies can significantly inhibit the growth and metastasis of established solid tumors, via the initiation of the antitumor immune responses in the body and the reversion of their immunosuppressive microenvironments. Considering the versatile surface engineering of porous silicon nanoparticles, the strategy developed here is beneficial to construct multifunctional nanovaccines with better biosafety and more diagnosis or therapeutic modalities against the occurrence, recurrence, or metastasis of solid tumors in future clinical practice

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Effect of Fixing Agent Dosage on the Mechanism of Colloidal Substances Retention onto Pulp

Three polyamine fixing agents with increasing molecular weights (m.w.), PA-Lw, PA-Mw, and PA-Hw, were used to treat a deinked pulp at three different levels of chemical dosage. The objective was to elucidate whether the retention mechanism of colloidal substances (CS) onto fibers by a fixing agent is different when the dosage is different. The results show that, for the polyamine with the lowest molecular weight (PA-Lw), it performed in the colloidal fixation mode over a wide range of dosage, but re-dispersion of CS took place in the pulp when its dosage was increased to a level high enough but still beneath the charge reversal point. For the polyamine with the highest m.w. (PA-Hw), CS re-dispersion was not observed over the whole dosage range, but a small part of the colloidal agglomeration coexisted with colloidal fixation even when the dosage was very low. For the polyamine with the middle m.w. (PA-Mw), both CS re-dispersion and colloidal agglomeration were observed. This study showed that if one wants to determine the dosage of a fixing agent during CS control better, both CS removal ratio and CS agglomeration behavior should be considered

High-Grade Tumor Budding Stratifies Early-Stage Cervical Cancer with Recurrence Risk

<div><p>Objectives</p><p>This study investigated prognostic significance of tumor budding in early-stage cervical cancer (ESCC) following radical surgery and its contribution to improve the stratification of patients with recurrence risk.</p><p>Methods</p><p>The archival medical records and H&E-stained slides of 643 patients with IA2-IIA stage cervical cancer who underwent radical surgery were retrospectively reviewed. Clinicopathological parameters were noted, and tumor buds were counted using immunohistochemistry for each case. The prognostic significance of tumor budding was analyzed. Prediction models that comprised tumor budding were established, and the performance was compared between the novel models and classic criteria via log-rank test and receiver operating characteristic analysis.</p><p>Results</p><p>Tumors with high-grade tumor budding (HTB) exhibited a substantially increased risk of recurrence (hazard ratio = 4.287, <i>P</i> < 0.001). Nine predictive models for recurrence were established, in which HTB was combined with recognized risk factors. The model using of at least two risk factors of HTB, tumor size ≥ 4 cm, deep stromal invasion of outer 1/3, and lymphovascular space invasion to stratify patients with an intermediate risk was most predictive of recurrence compared with the classic criteria.</p><p>Conclusions</p><p>Tumor budding is an independent, unfavorable, prognostic factor for ESCC patients following radical surgery and holds promise for improved recurrence risk stratification.</p></div