Federated NLP in Few-shot Scenarios

Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy-preserving setting (FL). This process currently relies on at least hundreds of thousands of labeled training samples from mobile clients; yet mobile users often lack willingness or knowledge to label their data. Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications. For the first time, this work investigates federated NLP in the few-shot scenario (FedFSL). By retrofitting algorithmic advances of pseudo labeling and prompt learning, we first establish a training pipeline that delivers competitive accuracy when only 0.05% (fewer than 100) of the training data is labeled and the remaining is unlabeled. To instantiate the workflow, we further present a system FFNLP, addressing the high execution cost with novel designs. (1) Curriculum pacing, which injects pseudo labels to the training workflow at a rate commensurate to the learning progress; (2) Representational diversity, a mechanism for selecting the most learnable data, only for which pseudo labels will be generated; (3) Co-planning of a model's training depth and layer capacity. Together, these designs reduce the training delay, client energy, and network traffic by up to 46.0$\times$, 41.2$\times$ and 3000.0$\times$, respectively. Through algorithm/system co-design, FFNLP demonstrates that FL can apply to challenging settings where most training samples are unlabeled

Towards Practical Few-shot Federated NLP

Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often requires a considerable amount of labeled private data. In practice, private data is often distributed across heterogeneous mobile devices and may be prohibited from being uploaded. Moreover, well-curated labeled data is often scarce, presenting an additional challenge. To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting. Subsequently, we propose AUG-FedPrompt, a prompt-based federated learning system that exploits abundant unlabeled data for data augmentation. Our experiments indicate that AUG-FedPrompt can perform on par with full-set fine-tuning with a limited amount of labeled data. However, such competitive performance comes at a significant system cost.Comment: EuroSys23 worksho

Determination of 30 Kinds of Antiparasitic Drugs in Animal-derived Foods by QuEChERS-UPLC-MS/MS

Objective: A method for determining the residues of 30 antiparasitic drugs in animal-derived food using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was established. Methods: The samples were extracted with acetonitrile and 1% ammonia acetic ether and purified by QuEChERS. After purification, Waters ACQUITY UPLCTM BEH C18 column was used for separation, and a gradient elution of 10 mmol/L ammonium formate (containing 0.1% formic acid) aqueous solution, acetonitrile:methanol (50:50, v:v) was performed as the mobile phase. Detection was carried out using electrospray ionization (ESI) in both positive and negative ion modes using multiple reaction monitoring (MRM). Matrix-matched external standard quantification was used. Results: Under the optimized conditions, the 30 antiparasitic drugs showed good linearity within their respective linear ranges, with coefficient of determination (r2) greater than 0.99. The recoveries ranged from 70.1% to 111%, and the relative standard deviations were between 0.10% and 9.1% (n=6). The method detection limit ranged from 0.001 to 0.3 μg/kg, and the method quantification limit ranged from 0.004 to 1 μg/kg. Conclusion: The method is sensitive, accurate, and exhibits good repeatability and stability, making it suitable for detecting various antiparasitic drug residues in animal-derived food

A study on the functions of ubiquitin metabolic system related gene FBG2 in gastric cancer cell line

<p>Abstract</p> <p>Background</p> <p>FBG2 (F-BOX6) gene is an important member in ubiquitin metabolic system F-BOX family, and forms E3 complex with the other members in the family. But its role in gastric cancer is still not clear. In the present study, we intended to investigate the influence of FBG2 on the growth, proliferation, apoptosis, invasion and cell cycle of the gastric cancer line MKN45 and gastric cell line HFE145.</p> <p>Methods</p> <p>As a critical component of ubiquitin-protein ligase complex, FBG2 cDNA was subcloned into a constitutive vector PCDNA3.1 followed by transfection in MKN45 and HFE145 by using liposome. Then stable transfectants were selected and appraised. The apoptosis and cell cycles of these clones were analyzed by using flow cytometry. The growth and proliferation were analyzed by cell growth curves and colony-forming assay respectively. The invasion of these clones was tested by using cancer cell migration assay. The FBG2 stable expression clones(MKN-FBG2 and HFE-FBG2) and their control groups were detected and compared respectively.</p> <p>Results</p> <p>MKN-FBG2 grew faster than MKN45 and MKN-PC(MKN45 transfected with PCDNA3.1 vector). HFE-FBG2 grew faster than HFE145 and HFE-PC(HFE145 transfected with PCDNA3.1 vector). The cell counts of MKN-FBG2 in the forth, fifth, sixth and seventh days were significantly more than those of others (P < 0.05). Cell cycle analysis showed that MKN-FBG2 and HFE-FBG2 proliferated faster, proportions of cells in G2-M and S were different significantly with control groups (P < 0.05). Results of colony-forming assay showed that the colony formation rates of MKN-FBG2 and HFE-FBG2 were higher than those of control groups (P < 0.05). The results of cell migration assay were all negative.</p> <p>Conclusion</p> <p>FBG2 can promote the growth and proliferation of gastric cancer cells and normal gastric cells. It can help tumor cell maintain malignant phenotype too. But it can have a negative influence on the apoptosis or the ability of invasion of gastric cancer cells.</p

Does a transformation approach improve students' ability in constructing auxiliary lines for solving geometric problems? An intervention-based study with two Chinese classrooms

We conducted an intervention-based study in secondary classrooms to explore whether the use of geometric transformations can help improve students’ ability in constructing auxiliary lines to solve geometric proof problems, especially high-level cognitive problems. A pre- and post-test quasi-experimental design was employed. The participants were 130 eighth-grade students in two classes with a comparable background that were taught by the same teacher. A two-week intervention was implemented in the experimental class aiming to help students learn how to use geometric transformations to draw auxiliary lines in solving geometric problems. The data were collected from a teacher interview, video-recordings of the intervention, and pre- and post- tests. The results revealed that there was a positive impact of using geometric transformations on the experimental students’ ability in solving high-level cognitive problems by adding auxiliary lines, though the impact on the students’ ability in solving general geometric problems as measured using the overall average scores was not statistically significant. Recommendations for future research are provided at the end of the article