Objective and Impact Statement:Human epidermal growth factor receptor 2(HER2)is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer(BC)and helps predict its prognosis.Here,we in...Objective and Impact Statement:Human epidermal growth factor receptor 2(HER2)is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer(BC)and helps predict its prognosis.Here,we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically(IHC)stained BC tissue images.Introduction:Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms.Nevertheless,the traditional workflow of manual examination by board-certified pathologists encounters challenges,including inter-and intra-observer inconsistency and extended turnaround times.Methods:Our deep learning-based method analyzes morphological features at various spatial scales,efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details.Results:This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view,leading to a blind testing classification accuracy of 84.70%,on a dataset of 523 core images from tissue microarrays.Conclusion:This automated system,proving reliable as an adjunct pathology tool,has the potential to enhance diagnostic precision and evaluation speed,and might substantially impact cancer treatment planning.展开更多
The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail...The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.展开更多
Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an imp...Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.展开更多
Synoptic patterns identified by an automated procedure employing principal- component analysis and a two-stage cluster analysis, and backward trajectory analysis clustered by the HYSPLIT4.9 model were used to examine ...Synoptic patterns identified by an automated procedure employing principal- component analysis and a two-stage cluster analysis, and backward trajectory analysis clustered by the HYSPLIT4.9 model were used to examine air quality patterns over¨ Uru¨mqi, China, one of the most heavily polluted cities in the world. Six synoptic patterns representing different atmospheric circulation patterns and air-mass characteristics were classified during the winter heating periods from 2001 to 2008, and seven trajectory clusters representing different paths of air masses arriving at ürümqi were calculated during the winter heating periods from 2005 to 2008. Then air quality was evaluated using these two approaches, and significant variations were found across both synoptic patterns and trajectory clusters. The heaviest air-pollution episodes occurred when ürümqi was either in an extremely cold, strong anticyclone or at the front of a migrating cyclone. Both conditions were characterized by with light winds, cold, wet surface air, and relatively dry upper air. ürümqi was predominately influenced by air masses from the southwest and from local areas. Air pollution index （API） levels were highest for air masses originating from the southwest with a longer path or for the local area, because of transport from semi-desert/desert regions by strong winds and because of local heavy pollution emissions, respectively. The interactions between these two analytical approaches showed that poor diffusion conditions, together with local circulation, enhanced air pollution, besides, regional air-mass transport caused by strong winds contributed to serious air quality under relatively good diffusion conditions.展开更多
To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved a...To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.展开更多
We propose the new experimental method for investigating and approximating the organization and structure of movements with given accuracy. The composition of approximating trajectories illuminating the movement trait...We propose the new experimental method for investigating and approximating the organization and structure of movements with given accuracy. The composition of approximating trajectories illuminating the movement traits discloses the level of movement expertise in dancers and golf players. The method allows estimating the level of movement expertise, drawing the detailed structure of movements, and classifying movements into a given repertoire automatically.展开更多
Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known ...Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.展开更多
An automated procedure employing principal-component analysis and a two-stage cluster analysis was developed to classify the synoptic meteorological conditions prevailing over Urumqi, one of the most heavily polluted ...An automated procedure employing principal-component analysis and a two-stage cluster analysis was developed to classify the synoptic meteorological conditions prevailing over Urumqi, one of the most heavily polluted cities in the world. Six clusters representing different circulation patterns and air-mass characteristics were classified using surface- and upper-meteorological variables during the heating period from 2001 to 2008, and the relationships between synoptic clusters and air quality were evaluated. The heaviest air-pollution episodes occurred when Urumqi was in either an extremely cold, strong anticyclone or at the front of a migrating cyclone, both with light winds, wet surface air, and relatively dry upper air. Moderate pollution was seen when Urumqi was in the pre-cold/cold frontal passages with lower temperatures and light winds or moderate anticyclone with relatively warmer, drier air. When Urumqi was at the front of a migrating anticyclone or in a weak anticyclone with moderate winds and most warm, dry air, or in the cold/post-cold frontal passages with relatively strongly northerly airflows and precipitation, relatively good air quality could be seen. These results suggest that air pollution in Urumqi is very closely related to the synoptic meteorological conditions, which provides an important basis for not only the prediction and control of urban air-quality problems here but also for the analysis of the differential impacts of weather and pollution on human morbidity.展开更多
基金supported by the NSF Biophotonics Program(to A.O.)the NIH National Center for Interventional Biophotonic Technologies(P41 to A.O.).
文摘Objective and Impact Statement:Human epidermal growth factor receptor 2(HER2)is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer(BC)and helps predict its prognosis.Here,we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically(IHC)stained BC tissue images.Introduction:Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms.Nevertheless,the traditional workflow of manual examination by board-certified pathologists encounters challenges,including inter-and intra-observer inconsistency and extended turnaround times.Methods:Our deep learning-based method analyzes morphological features at various spatial scales,efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details.Results:This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view,leading to a blind testing classification accuracy of 84.70%,on a dataset of 523 core images from tissue microarrays.Conclusion:This automated system,proving reliable as an adjunct pathology tool,has the potential to enhance diagnostic precision and evaluation speed,and might substantially impact cancer treatment planning.
文摘The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.
基金This work is supported by the National Natural Science Foundation of China(Nos.61771154,61603239,61772454,6171101570).
文摘Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.
基金supported by the National Basic Research Program (also called 973 Program) of China (Grant No 2007CB407303)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No KZCX2-YW-Q02-03)
文摘Synoptic patterns identified by an automated procedure employing principal- component analysis and a two-stage cluster analysis, and backward trajectory analysis clustered by the HYSPLIT4.9 model were used to examine air quality patterns over¨ Uru¨mqi, China, one of the most heavily polluted cities in the world. Six synoptic patterns representing different atmospheric circulation patterns and air-mass characteristics were classified during the winter heating periods from 2001 to 2008, and seven trajectory clusters representing different paths of air masses arriving at ürümqi were calculated during the winter heating periods from 2005 to 2008. Then air quality was evaluated using these two approaches, and significant variations were found across both synoptic patterns and trajectory clusters. The heaviest air-pollution episodes occurred when ürümqi was either in an extremely cold, strong anticyclone or at the front of a migrating cyclone. Both conditions were characterized by with light winds, cold, wet surface air, and relatively dry upper air. ürümqi was predominately influenced by air masses from the southwest and from local areas. Air pollution index （API） levels were highest for air masses originating from the southwest with a longer path or for the local area, because of transport from semi-desert/desert regions by strong winds and because of local heavy pollution emissions, respectively. The interactions between these two analytical approaches showed that poor diffusion conditions, together with local circulation, enhanced air pollution, besides, regional air-mass transport caused by strong winds contributed to serious air quality under relatively good diffusion conditions.
文摘To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.
文摘We propose the new experimental method for investigating and approximating the organization and structure of movements with given accuracy. The composition of approximating trajectories illuminating the movement traits discloses the level of movement expertise in dancers and golf players. The method allows estimating the level of movement expertise, drawing the detailed structure of movements, and classifying movements into a given repertoire automatically.
基金This work was supported by NASA MEaSUREs(grant number NNH13AV82I)U.S.Geological Survey provided sup-plemental funding from other direct and indirect means through its Land Change Science(LCS)Land Remote Sensing(LRS)programs as well as its Climate and Land Use Change Mission Area.
文摘Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX1-YW-06-01)
文摘An automated procedure employing principal-component analysis and a two-stage cluster analysis was developed to classify the synoptic meteorological conditions prevailing over Urumqi, one of the most heavily polluted cities in the world. Six clusters representing different circulation patterns and air-mass characteristics were classified using surface- and upper-meteorological variables during the heating period from 2001 to 2008, and the relationships between synoptic clusters and air quality were evaluated. The heaviest air-pollution episodes occurred when Urumqi was in either an extremely cold, strong anticyclone or at the front of a migrating cyclone, both with light winds, wet surface air, and relatively dry upper air. Moderate pollution was seen when Urumqi was in the pre-cold/cold frontal passages with lower temperatures and light winds or moderate anticyclone with relatively warmer, drier air. When Urumqi was at the front of a migrating anticyclone or in a weak anticyclone with moderate winds and most warm, dry air, or in the cold/post-cold frontal passages with relatively strongly northerly airflows and precipitation, relatively good air quality could be seen. These results suggest that air pollution in Urumqi is very closely related to the synoptic meteorological conditions, which provides an important basis for not only the prediction and control of urban air-quality problems here but also for the analysis of the differential impacts of weather and pollution on human morbidity.
