How to distribute total sum of funds among different investment priorities？ It is not only a theoretical problem in Management Accounting, but also a realistic problem in the investment decision of an enterprise. In ...How to distribute total sum of funds among different investment priorities？ It is not only a theoretical problem in Management Accounting, but also a realistic problem in the investment decision of an enterprise. In this paper, the author queries the method of ＂use linear programming to find out optimum combination＂, which put forward in management accounting, and gives a convenient and reasonable method---effective gradient method.展开更多
DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, dis...DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current activities.Then, they learn from their collaborations to improve their own performance in activity recognition.Conflict resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other agents.In this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .展开更多
Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging step.In this...Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging step.In this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images.ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework.Specifically,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision.Next,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features.To provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution reasoning.We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022.Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal karyotypes.When extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets.Given these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.展开更多
文摘How to distribute total sum of funds among different investment priorities？ It is not only a theoretical problem in Management Accounting, but also a realistic problem in the investment decision of an enterprise. In this paper, the author queries the method of ＂use linear programming to find out optimum combination＂, which put forward in management accounting, and gives a convenient and reasonable method---effective gradient method.
文摘DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current activities.Then, they learn from their collaborations to improve their own performance in activity recognition.Conflict resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other agents.In this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .
基金supported by the National Natural Science Foundation of China(No.81670137)SJTU Trans-med Awards Research(No.20220102)State Key Laboratory of Medical Genomics Support(No.201802)。
文摘Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging step.In this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images.ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework.Specifically,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision.Next,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features.To provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution reasoning.We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022.Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal karyotypes.When extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets.Given these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.
