Make Continual Learning Stronger via C-Flat

Model generalization ability upon incrementally acquiring dynamically updating knowledge from sequentially arriving tasks is crucial to tackle the sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape sharpness minimization seeking for flat minima lying in neighborhoods with uniform low loss or smooth gradient is proven to be a strong training regime improving model generalization compared with loss minimization based optimizer like SGD. Yet only a few works have discussed this training regime for CL, proving that dedicated designed zeroth-order sharpness optimizer can improve CL performance. In this work, we propose a Continual Flatness (C-Flat) method featuring a flatter loss landscape tailored for CL. C-Flat could be easily called with only one line of code and is plug-and-play to any CL methods. A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases. Code will be publicly available upon publication

Heavy metal characteristics of vegetables and their soils in Foshan City

Investigation of the vegetable garden soil in Foshan City 4 kinds of heavy metals Cu, Pb, Zn and Cd in the total and different forms of content, while also investigating a variety of vegetables and edible part of the Cd content. The results show that, Foshan City, the heavy metal content in vegetable field exceed the national and the background value of Guangdong Province, the pollution index to the maximum Cd, Cu, followed by, Cd elements of the highest validity coefficients. Foshan City, edible part of vegetables found excessive Cd, leafy soil Cd content and Cd the full amount of exchangeable manganese content and the amount of state showed a significant positive correlation. The state of heavy metal content of vegetable soil in Foshan city was investigated. The total content and available content of 4 heavy metal elements (Cd, Pb, Zn, and Cu) were analyzed and measured. The result indicated that the heavy metal content of vegetable soil in Foshan city was greater than the average in other areas throughout Guangdong Province or even the whole country. The valid coefficient of Cd element was the greatest. The content of Cd in vegetables was greater than the state vegetable sanitation standard. In different kinds of vegetables, the content of Cd in leaf-vegetable had very significant correlation with the content of different sort Cd in soil. which indicated that the content of Cd in vegetables was affected by the content of Cd in soil

Deubiquitinase PSMD14 enhances hepatocellular carcinoma growth and metastasis by stabilizing GRB2.

Hepatocellular carcinoma (HCC) has emerged as one of the most common malignancies worldwide. It is associated with a high mortality rate, as evident from its increasing incidence and extremely poor prognosis. The deubiquitinating enzyme 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) has been reported to act as an oncogene in several human cancers. The present study aimed to reveal the functional significance of PSMD14 in HCC progression and the underlying mechanisms. We found that PSMD14 was significantly upregulated in HCC tissues. Overexpression of PSMD14 correlated with vascular invasion, tumor number, tumor recurrence, and poor tumor-free and overall survival of patients with HCC. Knockdown and overexpression experiments demonstrated that PSMD14 promoted proliferation, migration, and invasion in HCC cells in vitro, and facilitated tumor growth and metastasis in vivo. Mechanistically, we identified PSMD14 as a novel post-translational regulator of GRB2. PSMD14 inhibits degradation of GRB2 via deubiquitinating this oncoprotein in HCC cells. Furthermore, pharmacological inhibition of PSMD14 with O-phenanthroline (OPA) suppressed the malignant behavior of HCC cells in vitro and in vivo. In conclusion, our findings suggest that PSMD14 could serve as a novel promising therapeutic candidate for HCC

Deubiquitinase PSMD14 enhances hepatocellular carcinoma growth and metastasis by stabilizing GRB2

Abstract(#br)Hepatocellular carcinoma (HCC) has emerged as one of the most common malignancies worldwide. It is associated with a high mortality rate, as evident from its increasing incidence and extremely poor prognosis. The deubiquitinating enzyme 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) has been reported to act as an oncogene in several human cancers. The present study aimed to reveal the functional significance of PSMD14 in HCC progression and the underlying mechanisms. We found that PSMD14 was significantly upregulated in HCC tissues. Overexpression of PSMD14 correlated with vascular invasion, tumor number, tumor recurrence, and poor tumor-free and overall survival of patients with HCC. Knockdown and overexpression experiments demonstrated that PSMD14 promoted proliferation, migration, and invasion in HCC cells in vitro , and facilitated tumor growth and metastasis in vivo . Mechanistically, we identified PSMD14 as a novel post-translational regulator of GRB2. PSMD14 inhibits degradation of GRB2 via deubiquitinating this oncoprotein in HCC cells. Furthermore, pharmacological inhibition of PSMD14 with O-phenanthroline (OPA) suppressed the malignant behavior of HCC cells in vitro and in vivo . In conclusion, our findings suggest that PSMD14 could serve as a novel promising therapeutic candidate for HCC

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Cycloaddition of carbon dioxide and epoxides catalyzed by rare earth metal complexes bearing a Trost ligand

Rare earth metal complexes containing Trost ligands were used to catalyze the cycloaddition reaction of epoxides with CO2. A series of epoxides were successfully converted into the corresponding cyclic carbonates under mild conditions.</p

Online Log Parsing Method Based on Bert and Adaptive Clustering

Log parsing is a technique for extracting valid information from raw log files,which can be used in areas such as system troubleshooting,performance analysis and security auditing.The main challenge of log parsing is the unstructured,diversity and dynamics of log data.Different systems and applications may use different log formats,and log formats may change over time.Therefore,this paper proposes BertLP,an online log parsing method that can automatically adapt to different log sources and log format variations.It uses a pre-trained language model,Bert,combined with an adaptive clustering algorithm for static and dynamic recognition of words in logs to group logs to generate log templates.Instead of manually defining log templates or regular expressions and performing frequency counts on words,BertLP automatically identifies log fields and types by learning semantic and structural features of log message.Comparative experiments on public log datasets show BertLP improves log parsing accuracy by 6.1% compared with the best available method and performs better on log parsing tasks