In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction ...Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.展开更多
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and ...Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are...Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are scarce.Therefore,we conducted a randomized controlled trial investigating the effects of aerobic or resistance exercise concomitant to neoadjuvant chemotherapy(NACT)on tumor size.Methods In the BENEFIT study(German title:Bewegung bei neoadjuvanter chemotherapie zur verbesserung der fitness),patients with breast cancer scheduled for NACT were randomly assigned to supervised resistance training(RT,n=60)or aerobic training(AT,n=60)twice weekly during NACT or to a waitlist control group(WCG,n=60).The primary outcome,“change in tumor size”,as well as the secondary clinical outcomes pathologic complete response(pCR),type of surgery(breast conserving/mastectomy),axillary lymph node dissection(ALND,yes/no),premature discontinuation of chemotherapy(yes/no),and relative dose intensity(RDI)were derived from clinical records.Due to the highly skewed distribution,the primary outcome was categorized.Multiple(ordinal)logistic regression analyses were performed.Results Overall,there was no significant difference in post-intervention tumor size between RT or AT and WCG.However,there was a significant effect modification by hormone receptor(HR)status(P_(interaction)=0.030).Among patients with HR+tumors,results suggest a beneficial effect of AT on tumor shrinkage(odds ratio(OR)=2.37,95%confidence interval(95%CI):0.97‒5.78),on pCR(OR=3.21,95%CI:0.97‒10.61);and on ALND(OR=3.76,95%CI:0.78‒18.06)compared to WCG.The effects of RT were slightly less pronounced.For HR−subtypes,beneficial effects on RDI were found for AT(OR=3.71,95%CI:1.20‒11.50)and similarly for RT(OR=2.58,95%CI:0.88‒7.59).Both AT and RT had favorable effects on premature discontinuation of chemotherapy(OR(no vs.yes)=2.34,95%CI:1.10‒5.06),irrespective of tumor receptor status.Conclusion While there was no significant effect on the primary outcome in the overall group,aerobic and resistance exercise concomitant to NACT seem to beneficially affect tumor shrinkage and pCR,reduce the need for ALND among patients with HR+breast cancers,and prevent low RDI among patients with HR–breast cancers.These results warrant confirmation in further trials.展开更多
This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related t...This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related to interval control valves(ICVs)in intelligent well completions(IWC).Transformer algorithms present significant advantages in time-series anomaly detection,primarily due to their ability to handle data drift and capture complex patterns effectively.Their self-attention mechanism allows these models to adapt to shifts in data distribution over time,ensuring resilience against changes that can occur in time-series data.Additionally,Transformers excel at identifying intricate temporal dependencies and long-range interactions,which are often challenging for traditional models.Field tests conducted in the ultradeep water subsea wells of the Santos Basin further validate the model’s capability for early anomaly identification of ICVs,minimizing non-productive time and safeguarding well integrity.The model achieved an accuracy of 0.9544,a balanced accuracy of 0.9694 and an F1-Score of 0.9574,representing significant improvements over previous literature models.展开更多
To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The findi...To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The finding is that "dialogue completion" patten has unpredicted answer and should be avoided in the testing as much as possible.展开更多
BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary app...BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary approaches in clinical practice may explain the recent increase in case reports and retrospective series documenting such responses.CASE SUMMARY This case series describes 3 patients with advanced HCC who achieved durable complete responses using first-line sorafenib therapy,even in the presence of portal vein thrombosis or extrahepatic spread,and highlights the potential for sustained remission in selected patients.Dermatologic toxicity and non-viral etiology may correlate with favorable outcomes;however,reliable predictive biomarkers for sorafenib response are lacking.CONCLUSION Future research into the etiology and molecular differences in HCC is necessary to develop more personalized therapy options.展开更多
International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and ...International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.Manuscript preparation The following components are required for a complete manuscript:Title,Author(s),Author affiliation(s),Abstract,Keywords,Main text,Acknowledgements and References.展开更多
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
基金supported by National Taiwan University Hospital Yunlin Branch Project NTUHYL 110.C018National Science and Technology Council,Taiwan.
文摘Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.
基金funded by Research Project,grant number BHQ090003000X03。
文摘Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金supported by an intramural proof of concept grant of the NCT Heidelberg.
文摘Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are scarce.Therefore,we conducted a randomized controlled trial investigating the effects of aerobic or resistance exercise concomitant to neoadjuvant chemotherapy(NACT)on tumor size.Methods In the BENEFIT study(German title:Bewegung bei neoadjuvanter chemotherapie zur verbesserung der fitness),patients with breast cancer scheduled for NACT were randomly assigned to supervised resistance training(RT,n=60)or aerobic training(AT,n=60)twice weekly during NACT or to a waitlist control group(WCG,n=60).The primary outcome,“change in tumor size”,as well as the secondary clinical outcomes pathologic complete response(pCR),type of surgery(breast conserving/mastectomy),axillary lymph node dissection(ALND,yes/no),premature discontinuation of chemotherapy(yes/no),and relative dose intensity(RDI)were derived from clinical records.Due to the highly skewed distribution,the primary outcome was categorized.Multiple(ordinal)logistic regression analyses were performed.Results Overall,there was no significant difference in post-intervention tumor size between RT or AT and WCG.However,there was a significant effect modification by hormone receptor(HR)status(P_(interaction)=0.030).Among patients with HR+tumors,results suggest a beneficial effect of AT on tumor shrinkage(odds ratio(OR)=2.37,95%confidence interval(95%CI):0.97‒5.78),on pCR(OR=3.21,95%CI:0.97‒10.61);and on ALND(OR=3.76,95%CI:0.78‒18.06)compared to WCG.The effects of RT were slightly less pronounced.For HR−subtypes,beneficial effects on RDI were found for AT(OR=3.71,95%CI:1.20‒11.50)and similarly for RT(OR=2.58,95%CI:0.88‒7.59).Both AT and RT had favorable effects on premature discontinuation of chemotherapy(OR(no vs.yes)=2.34,95%CI:1.10‒5.06),irrespective of tumor receptor status.Conclusion While there was no significant effect on the primary outcome in the overall group,aerobic and resistance exercise concomitant to NACT seem to beneficially affect tumor shrinkage and pCR,reduce the need for ALND among patients with HR+breast cancers,and prevent low RDI among patients with HR–breast cancers.These results warrant confirmation in further trials.
文摘This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related to interval control valves(ICVs)in intelligent well completions(IWC).Transformer algorithms present significant advantages in time-series anomaly detection,primarily due to their ability to handle data drift and capture complex patterns effectively.Their self-attention mechanism allows these models to adapt to shifts in data distribution over time,ensuring resilience against changes that can occur in time-series data.Additionally,Transformers excel at identifying intricate temporal dependencies and long-range interactions,which are often challenging for traditional models.Field tests conducted in the ultradeep water subsea wells of the Santos Basin further validate the model’s capability for early anomaly identification of ICVs,minimizing non-productive time and safeguarding well integrity.The model achieved an accuracy of 0.9544,a balanced accuracy of 0.9694 and an F1-Score of 0.9574,representing significant improvements over previous literature models.
文摘To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The finding is that "dialogue completion" patten has unpredicted answer and should be avoided in the testing as much as possible.
文摘BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary approaches in clinical practice may explain the recent increase in case reports and retrospective series documenting such responses.CASE SUMMARY This case series describes 3 patients with advanced HCC who achieved durable complete responses using first-line sorafenib therapy,even in the presence of portal vein thrombosis or extrahepatic spread,and highlights the potential for sustained remission in selected patients.Dermatologic toxicity and non-viral etiology may correlate with favorable outcomes;however,reliable predictive biomarkers for sorafenib response are lacking.CONCLUSION Future research into the etiology and molecular differences in HCC is necessary to develop more personalized therapy options.
文摘International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.Manuscript preparation The following components are required for a complete manuscript:Title,Author(s),Author affiliation(s),Abstract,Keywords,Main text,Acknowledgements and References.
