Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Multiple drug resistance(MDR)is the tumor’s way of escaping the cytotoxic effects of various unrelated chemotherapeutic drugs.It can be either innate or acquired.MDR represents the end of the therapeutic pathway,and ...Multiple drug resistance(MDR)is the tumor’s way of escaping the cytotoxic effects of various unrelated chemotherapeutic drugs.It can be either innate or acquired.MDR represents the end of the therapeutic pathway,and it practically leaves no treatment alternatives.Reversing MDR is an unfulfilled goal,despite the important recent advances in cancer research.MDR,the main cause of death in cancer patients,is a multi-factorial development,and most of its known causes have been thoroughly discussed in the literature.However,there is one aspect that has not received adequate consideration-intracellular alkalosis-which is part of wider pH deregulation where the pH gradient is inverted,meaning that extracellular pH is decreased and intracellular pH increased.This situation interacts with MDR and with the proteins involved,such as P-gp,breast cancer resistance protein,and multidrug associated resistance protein 1.However,there are also situations in which these proteins play no role at all,and where pH takes the lead.This is the case in ion trapping.Reversing the pH gradient to normal can be an important contribution to managing MDR.The drugs to manipulate pH exist,and most of them are FDA approved and in clinical use for other purposes.Furthermore,they have low or no toxicity and are inexpensive compared with any chemotherapeutic treatment.Repurposing these drugs and combining them in a reasonable fashion is one of the points proposed in this paper,which discusses the relationship between cancer’s peculiar pH and MDR.展开更多
This study documents the discovery of mound morphologies containing gas hydrate and methane-derived authigenic carbonate(MDAC)in the southwestern slope of the Chukchi Plateau,during the IBRV Araon expeditions in 2016 ...This study documents the discovery of mound morphologies containing gas hydrate and methane-derived authigenic carbonate(MDAC)in the southwestern slope of the Chukchi Plateau,during the IBRV Araon expeditions in 2016 and 2018.A multibeam bathymetric surveying was the basis for a new and detailed rendering around the mounds.Sub-bottom profiles and site-targeted gravity cores were also collected across these mounds which were located at water depths between 780 m and 580 m.Mounds are characterized by a circular plan shape of hundreds of meters in width and tens of meters in height.Below the mounds,gas accumulation in the sediment produces acoustic blanking in seismic data.MDACs are identified along the core collected from the top of a mound structure,indicating past methane oxidation processes.Gas hydrate has also been observed at the bottom of the core.Reverse geothermal gradients of the mound support the idea of active presentday seepage.We argue that the prolonged seepage activity of methane-rich fluid,possibly related to the formation of the rifted North Chukchi Basin,has led to the formation of the gas hydrate mounds,named hereafter the Araon Mounds,in the vicinity of the basin margin.展开更多
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
文摘Multiple drug resistance(MDR)is the tumor’s way of escaping the cytotoxic effects of various unrelated chemotherapeutic drugs.It can be either innate or acquired.MDR represents the end of the therapeutic pathway,and it practically leaves no treatment alternatives.Reversing MDR is an unfulfilled goal,despite the important recent advances in cancer research.MDR,the main cause of death in cancer patients,is a multi-factorial development,and most of its known causes have been thoroughly discussed in the literature.However,there is one aspect that has not received adequate consideration-intracellular alkalosis-which is part of wider pH deregulation where the pH gradient is inverted,meaning that extracellular pH is decreased and intracellular pH increased.This situation interacts with MDR and with the proteins involved,such as P-gp,breast cancer resistance protein,and multidrug associated resistance protein 1.However,there are also situations in which these proteins play no role at all,and where pH takes the lead.This is the case in ion trapping.Reversing the pH gradient to normal can be an important contribution to managing MDR.The drugs to manipulate pH exist,and most of them are FDA approved and in clinical use for other purposes.Furthermore,they have low or no toxicity and are inexpensive compared with any chemotherapeutic treatment.Repurposing these drugs and combining them in a reasonable fashion is one of the points proposed in this paper,which discusses the relationship between cancer’s peculiar pH and MDR.
基金supported by the KIMST Grant 20160247.Y.-G.Kim was also supported by the KMA Research and Development Program(KMI2018-02110)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2019R1A6A1A03033167)the Young Researcher Program through the NRF grant funded by the Korea government(MSIT)(No.2020R1C1C1007495).
文摘This study documents the discovery of mound morphologies containing gas hydrate and methane-derived authigenic carbonate(MDAC)in the southwestern slope of the Chukchi Plateau,during the IBRV Araon expeditions in 2016 and 2018.A multibeam bathymetric surveying was the basis for a new and detailed rendering around the mounds.Sub-bottom profiles and site-targeted gravity cores were also collected across these mounds which were located at water depths between 780 m and 580 m.Mounds are characterized by a circular plan shape of hundreds of meters in width and tens of meters in height.Below the mounds,gas accumulation in the sediment produces acoustic blanking in seismic data.MDACs are identified along the core collected from the top of a mound structure,indicating past methane oxidation processes.Gas hydrate has also been observed at the bottom of the core.Reverse geothermal gradients of the mound support the idea of active presentday seepage.We argue that the prolonged seepage activity of methane-rich fluid,possibly related to the formation of the rifted North Chukchi Basin,has led to the formation of the gas hydrate mounds,named hereafter the Araon Mounds,in the vicinity of the basin margin.
